Machining engineer jobs in San Francisco, CA - 12,608 jobs

Cupertino, California, United States At Apple we believe our products begin with our people. By hiring a diverse team, we drive creative thought. By giving that team everything they need, we drive innovation. By hiring incredible engineers, we drive precision. And through our collaborative process, we build memorable experiences for our customers! These elements come together to make Apple an amazing environment for motivated people to do the greatest work of their lives. You will become part of a hands‑on development team that sets the standard in cultivating excellence, creativity and innovation. Come help us design the next generation of revolutionary Apple products. We are looking for a forward‑thinking and unusually talented engineer. As a member of our dynamic group, you will have the rare and rewarding opportunity to craft and implement methodologies and solutions with a high impact on upcoming products that will delight and inspire millions of Apple's customers every single day. In this role, you will be directly involved in our SoC design machine learning efforts, collaborating right alongside our internal multi‑functional teams, and using your expertise in machine learning to improve productivity and optimizations across several SoC design related functions spanning from design to validation. Description As a member of the SoC design machine learning team, you will be part of a dynamic team that is building the most efficient application processors on the planet, powering the next generation of Apple products. Your expertise in machine learning will be instrumental in optimizing for efficiency, quality and speed for our chip‑design process. You'll play a crucial role in developing generative AI and machine learning solutions for optimizations in RTL Design, Verification, and Power/Performance/Area efforts. You will collaborate closely with our internal multi‑functional teams as well as the AIML organization at Apple to understand domain‑specific needs and tailor machine learning solutions to these domains. As part of this role, you will keep abreast of emerging technologies in machine learning and chip design to ensure our solutions remain state‑of‑the‑art. Prior leadership experience is a plus. Minimum Qualifications Minimum of BS + 3 years relevant industry experience. Preferred Qualifications Practical experience and knowledge of generative AI and modern machine learning methods. Experience with any of the following: RAG systems including embedding models, retrieval strategies, and context optimization techniques. Generative AI pipeline development. AI evaluation and testing proficiency including designing test suites and implementing human evaluation frameworks. Experience with AI coding assistants. Experience with AI infrastructure protocols such as MCP. Experience with deep neural networks and reinforcement learning is a plus. Solid math background and understanding of algorithms and data structures. Experience with current deep learning frameworks, such as PyTorch, TensorFlow, JAX or MLX. Experience with hardware description languages like Verilog, SystemVerilog or VHDL. Experience with Chip design and verification front end flows is a plus: Working verification experience with UVM testbenches. Working experience with front‑end tools such as Static timing analysis and CDC/RDC. Strong communication and collaboration skills, with the ability to work efficiently in cross‑functional teams. Master's or PhD with relevant publications preferred but not required. At Apple, base pay is one part of our total compensation package and is determined within a range. This provides the opportunity to progress as you grow and develop within a role. The base pay range for this role is between $147,400 and $272,100, and your base pay will depend on your skills, qualifications, experience, and location. Apple employees also have the opportunity to become an Apple shareholder through participation in Apple's discretionary employee stock programs. Apple employees are eligible for discretionary restricted stock unit awards, and can purchase Apple stock at a discount if voluntarily participating in Apple's Employee Stock Purchase Plan. You'll also receive benefits including comprehensive medical and dental coverage, retirement benefits, a range of discounted products and free services, and for formal education related to advancing your career at Apple, reimbursement for certain educational expenses - including tuition. Additionally, this role might be eligible for discretionary bonuses or commission payments as well as relocation. Learn more about Apple Benefits. Note: Apple benefit, compensation and employee stock programs are subject to eligibility requirements and other terms of the applicable plan or program. Apple is an equal opportunity employer that is committed to inclusion and diversity. We seek to promote equal opportunity for all applicants without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, Veteran status, or other legally protected characteristics. Learn more about your EEO rights as an applicant. Apple accepts applications to this posting on an ongoing basis. #J-18808-Ljbffr

Help us build the systems that train specialized AI models for the fastest-growing companies in the world. If you love taking cutting-edge ML techniques and turning them into products that ship, we'd love to meet you. net Inference.net trains and hosts specialized language models for companies who want frontier-quality AI at a fraction of the cost. The models we train match GPT-5 accuracy but are smaller, faster, and up to 90% cheaper. Our platform handles everything end-to-end: distillation, training, evaluation, and planet-scale hosting. We are a well-funded ten-person team of engineers who work in-person in downtown San Francisco on difficult, high-impact engineering problems. Everyone on the team has been writing code for over 10 years, and has founded and run their own software companies. We are high-agency, adaptable, and collaborative. We value creativity alongside technical prowess and humility. We work hard, and deeply enjoy the work that we do. Most of us are in the office 4 days a week in SF; hybrid works for Bay Area candidates. About the Role You will be responsible for building and improving the core ML systems that power our custom model training platform, while also applying these systems directly for customers. Your role sits at the intersection of applied research and production engineering. You'll lead projects from data intake to trained model, building the infrastructure and tooling along the way. Your north star is model quality at scale, measured by how well our custom models match frontier performance, how efficiently we can train and serve them, and how smoothly we can deliver results to our customers. You'll own the full training lifecycle: processing data, creating dashboards for visibility, training models using our frameworks, running evaluations, and shipping results. This role reports directly to the founding team. You'll have the autonomy, a large compute budget / GPU reservation, and technical support to push the boundaries of what's possible in custom model training. Key Responsibilities Lead projects from data intake through the full training pipeline, including processing, cleaning, and preparing datasets for model training Build and maintain data processing pipelines for aggregating, transforming, and validating training data Create dashboards and visualization tools to display training metrics, data quality, and model performance Train models using our internal frameworks and iterate based on evaluation results Develop robust benchmarks and evaluation frameworks that ensure custom models match or exceed frontier performance Build systems to automate portions of the training workflow, reducing manual intervention and improving consistency Take research features and ship them into production settings Apply the latest techniques in SFT, RL, and model optimization to improve training quality and efficiency Collaborate with infrastructure engineers to scale training across our GPU fleet Deeply understand customer use cases to inform training strategies and surface edge cases Requirements 2+ years of experience training AI models using PyTorch Hands‑on experience with post‑training LLMs using SFT or RL Strong understanding of transformer architectures and how they're trained Experience with LLM‑specific training frameworks (e.g., Hugging Face Transformers, DeepSpeed, Axolotl, or similar) Experience training on NVIDIA GPUs Strong data processing skills and comfortable building ETL pipelines and working with large datasets Track record of creating benchmarks and evaluations Ability to take research techniques and apply them to production systems Nice-to-Have Experience with model distillation or knowledge transfer Experience building dashboards and data visualization tools Familiarity with vision encoders and multimodal models Experience with distributed training at scale Contributions to open‑source ML projects You don't need to tick every box. Curiosity and the ability to learn quickly matter more. Compensation We offer competitive compensation, equity in a high-growth startup, and comprehensive benefits. The base salary range for this role is $220,000 - $320,000, plus equity and benefits, depending on experience. Equal Opportunity Inference.net is an equal opportunity employer. We welcome applicants from all backgrounds and don't discriminate based on race, color, religion, gender, sexual orientation, national origin, genetics, disability, age, or veteran status. If you're excited about building the future of custom AI infrastructure, we'd love to hear from you. Please send your resume and GitHub to ****************** and/or apply here on Ashby. #J-18808-Ljbffr

Motive Software | Motive Software | Posted Jan 14, 2026 Apply Full-time Negotiable Advanced (5-10 yrs) Let's face it, a company whose mission is human transformation better have some fresh thinking about the employer/employee relationship. We do. We can't cram it all in here, but you'll start noticing it from the first interview. Even our candidate experience is different. And when you get an offer from us (and accept it), you get way more than a paycheck. You get a personal BetterUp Coach, a development plan, a trained and coached manager, the most amazing team you've ever met (yes, each with their own personal BetterUp Coach), and most importantly, work that matters. This makes for a remarkably focused and fulfilling work experience. Frankly, it's not for everyone. But for people with fire in their belly, it's a game‑changing, career‑defining, soul‑lifting move. Join us and we promise you the most intense and fulfilling years of your career, doing life‑changing work in a fun, inventive, soulful culture. If that sounds exciting-and the job description below feels like a fit-we really should start talking. AI is transforming the future of coaching-and BetterUp is at the frontiers of that transformation. We're seeking a creative, product‑minded machine learning engineer who's eager to roll up their sleeves and help shape what comes next alongside our world‑class team. The ideal candidate is motivated by impact, thrives in fast‑paced and ambiguous environments, and brings clarity where there's complexity. You're someone who values empowering your teammates as much as your own growth, and who stays focused on what matters most: delivering meaningful, human‑centered value to our members and customers. At BetterUp, we support and challenge one another to grow, always with empathy and excellence. We're looking for someone who shares our commitment to continuous learning, who takes pride in craftspersonship, and who believes deeply in the power of technology to drive positive change in the world. And because we know peak performance comes from balance, we foster a culture that supports you in bringing your full self to our mission. We are a hybrid company with a focus on in‑person collaboration when necessary. Employees are expected to be available to work from one of our office hubs at least two days per week, or eight days per month. Our U.S. hub locations include: Austin, TX; New York City, NY; San Francisco, CA; and the Arlington, VA metro area. Please ensure you can realistically commit to this structure before applying. What You'll Do Design and deliver state‑of‑the‑art Generative AI systems and experiences that set the standard for quality, engagement, and trust in coaching. Mentor and elevate engineers at all levels through technical guidance, code and design reviews, and thought partnership, raising the bar for engineering excellence. Collaborate deeply with cross‑functional partners across product, research, design, and engineering, to ship and scale first‑of‑their‑kind AI coaching products. Shape direction and scope by developing a deep understanding of customer needs and translating them into technical strategy and product roadmaps. Stay ahead of the curve in Generative AI research and practice, leading conversations on emerging technologies and how to responsibly bring them into production. Qualifications 7+ years as an ML engineer and 3+ years of hands‑on experience building and shipping products on top of leading LLMs Proven track record designing, implementing, and evaluating LLM outputs Experience fine‑tuning smaller models or adapting foundation models to improve quality, engagement, and scalability Background at a leading AI research lab or equivalent experience staying at the forefront of Applied ML/AI, GenAI, with strong software engineering skills and a deep understanding of LLM internals A high degree of initiative and ownership, combined with the ability to navigate ambiguity and adapt quickly to change Experience working collaboratively in a cross‑functional team and with people at all levels in an organization Alignment with BetterUp's mission of enabling individuals to maximize their potential Ability to communicate complex ideas effectively to both technical and non‑technical audiences, verbally and in writing A passion for continuous learning, customer empathy, and a desire to innovate within a fast‑paced environment. Benefits Access to BetterUp coaching; one for you and one for a friend or family member A competitive compensation plan with opportunity for advancement Medical, dental, and vision insurance Flexible paid time off Per year: All federal/statutory holidays observed 4 BetterUp Inner Workdays (*********************************** 5 Volunteer Days to give back Learning and Development stipend Company wide Summer & Winter breaks Year‑round charitable contribution of your choice on behalf of BetterUp 401(k) self‑contribution Additional Information We are dedicated to building diverse teams that fuel an authentic workplace and sense of belonging for each and every employee. We know applying for a job can be intimidating, please don't hesitate to reach out - we encourage everyone interested in joining us to apply. BetterUp Inc. provides equal employment opportunities (EEO) to all employees and applicants for employment without regard to race, color, religion, sex, national origin, disability, genetics, gender, sexual orientation, age, marital status, veteran status. In addition to federal law requirements, BetterUp Inc. complies with applicable state and local laws governing nondiscrimination in employment in every location in which the company has facilities. This policy applies to all terms and conditions of employment, including recruiting, hiring, placement, promotion, termination, layoff, recall, transfer, leaves of absence, compensation, and training. At BetterUp, we compensate our employees fairly for their work. Base salary is determined by job‑related experience, education/training, residence location, as well as market indicators. The range below is representative of base salary only and does not include equity, sales bonus plans (when applicable) and benefits. This range may be modified in the future. The base salary range for this role is as follows: New York City and San Francisco: $228,000 - $313,500 Austin, Arlington, and Chicago: $205,200 - $282,150 Protecting your privacy and treating your personal information with care is very important to us, and central to the entire BetterUp family. By submitting your application, you acknowledge that your personal information will be processed in accordance with our Applicant Privacy Notice. If you have any questions about the privacy of your personal information or your rights with regards to your personal information, please reach out to *******************. About the company Motive Software #J-18808-Ljbffr

Employment Type Full time Department Engineering Compensation Estimated Base Salary $220K - $320K • Offers Equity Help us build the systems that train specialized AI models for the fastest-growing companies in the world. If you love taking cutting‑edge ML techniques and turning them into products that ship, we'd love to meet you. About Inference.net Inference.net trains and hosts specialized language models for companies who want frontier-quality AI at a fraction of the cost. The models we train match GPT‑5 accuracy but are smaller, faster, and up to 90% cheaper. Our platform handles everything end‑to‑end: distillation, training, evaluation, and planet‑scale hosting. We are a well‑funded ten‑person team of engineers who work in‑person in downtown San Francisco on difficult, high‑impact engineering problems. Everyone on the team has been writing code for over 10 years, and has founded and run their own software companies. We are high‑agency, adaptable, and collaborative. We value creativity alongside technical prowess and humility. We work hard, and deeply enjoy the work that we do. Most of us are in the office 4 days a week in SF; hybrid works for Bay Area candidates. About the Role You will be responsible for building and improving the core ML systems that power our custom model training platform, while also applying these systems directly for customers. Your role sits at the intersection of applied research and production engineering. You'll lead projects from data intake to trained model, building the infrastructure and tooling along the way. Your north star is model quality at scale, measured by how well our custom models match frontier performance, how efficiently we can train and serve them, and how smoothly we can deliver results to our customers. You'll own the full training lifecycle: processing data, creating dashboards for visibility, training models using our frameworks, running evaluations, and shipping results. This role reports directly to the founding team. You'll have the autonomy, a large compute budget / GPU reservation, and technical support to push the boundaries of what's possible in custom model training. Key Responsibilities Lead projects from data intake through the full training pipeline, including processing, cleaning, and preparing datasets for model training Build and maintain data processing pipelines for aggregating, transforming, and validating training data Create dashboards and visualization tools to display training metrics, data quality, and model performance Train models using our internal frameworks and iterate based on evaluation results Develop robust benchmarks and evaluation frameworks that ensure custom models match or exceed frontier performance Build systems to automate portions of the training workflow, reducing manual intervention and improving consistency Take research features and ship them into production settings Apply the latest techniques in SFT, RL, and model optimization to improve training quality and efficiency Collaborate with infrastructure engineers to scale training across our GPU fleet Deeply understand customer use cases to inform training strategies and surface edge cases Requirements 2+ years of experience training AI models using PyTorch Hands‑on experience with post‑training LLMs using SFT or RL Strong understanding of transformer architectures and how they're trained Experience with LLM‑specific training frameworks (e.g., Hugging Face Transformers, DeepSpeed, Axolotl, or similar) Experience training on NVIDIA GPUs Strong data processing skills and comfortable building ETL pipelines and working with large datasets Track record of creating benchmarks and evaluations Ability to take research techniques and apply them to production systems Nice‑to‑Have Experience with model distillation or knowledge transfer Experience building dashboards and data visualization tools Familiarity with vision encoders and multimodal models Experience with distributed training at scale Contributions to open‑source ML projects You don't need to tick every box. Curiosity and the ability to learn quickly matter more. Compensation We offer competitive compensation, equity in a high‑growth startup, and comprehensive benefits. The base salary range for this role is $220,000 - $320,000, plus equity and benefits, depending on experience. Equal Opportunity Inference.net is an equal opportunity employer. We welcome applicants from all backgrounds and don't discriminate based on race, color, religion, gender, sexual orientation, national origin, genetics, disability, age, or veteran status. If you're excited about building the future of custom AI infrastructure, we'd love to hear from you. Please send your resume and GitHub to ****************** and/or apply here on Ashby. Compensation Range: $220K - $320K #J-18808-Ljbffr

About Quizlet: At Quizlet, our mission is to help every learner achieve their outcomes in the most effective and delightful way. Our $1B+ learning platform serves tens of millions of students every month, including two-thirds of U.S. high schoolers and half of U.S. college students, powering over 2 billion learning interactions monthly. We blend cognitive science with machine learning to personalize and enhance the learning experience for students, professionals, and lifelong learners alike. We're energized by the potential to power more learners through multiple approaches and various tools. Let's Build the Future of Learning Join us to design and deliver AI‑powered learning tools that scale across the world and unlock human potential. About the Team: The Personalization & Recommendations ML Engineering team builds the core intelligence behind how Quizlet matches learners with content, activities and experiences that best fit their goals. We power recommendation and search systems across multiple surfaces, from home feed and search results to adaptive study modes. Our mission is to make Quizlet feel uniquely tailored for every learner by combining cutting‑edge machine learning, scalable infrastructure and insights from learning science. You'll collaborate closely with product managers, data scientists, platform engineers and fellow ML engineers to deliver personalized learning pathways that drive engagement, satisfaction and measurable learning outcomes. About the Role: As a Senior or Staff Machine Learning Engineer on the Personalization & Recommendations team, you'll design and build large‑scale retrieval, ranking, and recommendation systems that directly shape how learners discover and engage with Quizlet. You'll bring deep expertise in modern recommender systems - from deep learning‑based retrieval and embeddings to multi‑task ranking and evaluation - and help evolve Quizlet's personalization stack to power adaptive, effective learning experiences. You'll work at the intersection of machine learning, product design, and scalable systems, ensuring our recommendations are performant, responsible, and aligned with learner outcomes, privacy, and fairness. We're happy to share that this is an onsite position in our San Francisco office. To help foster team collaboration, we require that employees be in the office a minimum of three days per week: Monday, Wednesday, and Thursday and as needed by your manager or the company. We believe that this working environment supports collaboration and accountability. In this role, you will: Design and implement personalization models across candidate retrieval, ranking, and post‑ranking layers, leveraging user embeddings, contextual signals, and content features Develop scalable retrieval and serving systems using architectures such as Two‑Tower, deep ranking, and ANN‑based vector search for real‑time personalization across surfaces Build and maintain model training, evaluation, and deployment pipelines, ensuring reliability, training‑serving consistency, and robust monitoring Partner closely with Product and Data Science to translate learner objectives (engagement, retention, mastery) into measurable modeling goals and experimentation plans Advance evaluation methodologies, refining offline metrics (e.g., NDCG, CTR, calibration) and supporting rigorous A/B testing to measure learner and business impact Collaborate with platform and infrastructure teams to optimize distributed training, inference latency, and serving cost at scale Contribute to the long‑term technical vision for personalization and recommendations, aligning modeling strategy with Quizlet's AI and product roadmaps Stay current with RecSys research and industry trends, bringing relevant advances from top conferences (KDD, WSDM, SIGIR, RecSys, NeurIPS) into production Mentor other engineers and applied scientists, fostering technical growth, experimentation rigor, and responsible ML practices Champion collaboration, inclusion, and curiosity, helping build a team culture that values diverse perspectives and data‑driven problem‑solving What you bring to the table: 10+ years of experience in applied machine learning or ML‑heavy software engineering, with a strong focus on personalization, ranking, or recommendation systems Track record of measurable impact, improving key online metrics such as CTR, retention, or engagement through recommender or search systems in production Strong hands‑on skills in Python and PyTorch, with expertise in data and feature engineering, distributed training and inference on GPUs, and familiarity with modern MLOps practices - including model registries, feature stores, monitoring, and drift detection Deep understanding of retrieval and ranking architectures, including Two‑Tower models, deep cross networks, Transformers, or MMoE, and how to apply them in production contexts Experience with large‑scale embedding models and vector search (e.g., FAISS, ScaNN), including training, serving, and optimization at scale Proficiency in experiment design and evaluation, connecting offline metrics (AUC, NDCG, calibration) with online A/B test results to drive product decisions Ability to communicate complex technical ideas clearly, collaborating effectively with product managers, data scientists, and engineers across teams Growth and mentorship mindset, contributing to team learning and helping raise the bar for modeling quality, experimentation, and reliability Commitment to responsible and inclusive personalization, ensuring our ML systems respect learner privacy, fairness, and diverse goals Bonus points if you have: Publications or open‑source contributions in RecSys, search, or ranking Familiarity with reinforcement learning for recommendations or contextual bandits Experience with hybrid RecSys systems blending collaborative filtering, content understanding, and LLM‑based reasoning Prior work in consumer or EdTech applications with personalization at scale Compensation, Benefits & Perks: Quizlet is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees. Salary transparency helps to mitigate unfair hiring practices when it comes to discrimination and pay gaps. Total compensation for this role is market competitive, including a starting base salary of $209,920 - $285,000, depending on location and experience, as well as company stock options Collaborate with your manager and team to create a healthy work‑life balance 20 vacation days that we expect you to take! Competitive health, dental, and vision insurance (100% employee and 75% dependent PPO, Dental, VSP Choice) Employer‑sponsored 401k plan with company match Access to LinkedIn Learning and other resources to support professional growth Paid Family Leave, FSA, HSA, Commuter benefits, and Wellness benefits 40 hours of annual paid time off to participate in volunteer programs of choice Why Join Quizlet? 🌎 Massive reach: 60M+ users, 1B+ interactions per week 🧠 Cutting‑edge tech: Generative AI, adaptive learning, cognitive science 📈 Strong momentum: Top‑tier investors, sustainable business, real traction 🎯 Mission‑first: Work that makes a difference in people's lives 🤝 Inclusive culture: Committed to equity, diversity, and belonging We strive to make everyone feel comfortable and welcome! We work to create a holistic interview process, where both Quizlet and candidates have an opportunity to view what it would be like to work together, in exploring a mutually beneficial partnership. We provide a transparent setting that gives a comprehensive view of who we are! In Closing: At Quizlet, we're excited about passionate people joining our team-even if you don't check every box on the requirements list. We value unique perspectives and believe everyone has something meaningful to contribute. Our culture is all about taking initiative, learning through challenges, and striving for high‑quality work while staying curious and open to new ideas. We believe in honest, respectful communication, thoughtful collaboration, and creating a supportive space where everyone can grow and succeed together. Quizlet's success as an online learning community depends on a strong commitment to diversity, equity, and inclusion. As an equal opportunity employer and a tech company committed to societal change, we welcome applicants from all backgrounds. Women, people of color, members of the LGBTQ+ community, individuals with disabilities, and veterans are strongly encouraged to apply. Come join us! To All Recruiters and Placement Agencies: At this time, Quizlet does not accept unsolicited agency resumes and/or profiles. Please do not forward unsolicited agency resumes to our website or to any Quizlet employee. Quizlet will not pay fees to any third‑party agency or firm nor will it be responsible for any agency fees associated with unsolicited resumes. All unsolicited resumes received will be considered the property of Quizlet. #J-18808-Ljbffr

Machine Learning Engineer, Level 5 page is loaded## Machine Learning Engineer, Level 5locations: Palo Alto, California: New York - 229 W 43rd St: Bellevue - 110 110th Ave NE: Seattle - 2025 1st Avenue: San Francisco - 1160 Battery Sttime type: Full timeposted on: Posted Todaytime left to apply: End Date: March 31, 2026 (30+ days left to apply)job requisition id: Q126SWEML5is a technology company. We believe the camera presents the greatest opportunity to improve the way people live and communicate. Snap contributes to human progress by empowering people to express themselves, live in the moment, learn about the world, and have fun together. The Company's three core products are , a visual messaging app that enhances your relationships with friends, family, and the world; , an augmented reality platform that powers AR across Snapchat and other services; and its AR glasses, .teams build fun and technically sophisticated products that reach hundreds of millions of Snapchatters around the world, every day. We're deeply committed to the well-being of everyone in our global community, which is why are at the root of everything we do. We move fast, with precision, and always execute with privacy at the forefront.We're looking for a Machine Learning Engineer to join Snap Inc!What you'll do:* Build and deploy machine learning models that power core products, serving millions of Snapchatters* Apply modern ML techniques to solve large-scale, real-world problems* Own the full ML lifecycle from data analysis to production deployment* Partner with cross-functional teams to prototype and launch ML-driven features Knowledge, Skills & Abilities:* Strong understanding of machine learning approaches and algorithms* Able to prioritize duties and work well on your own* Ability to work with both internal and external partners* Skilled at solving open ambiguous problems* Strong collaboration and mentorship skills Minimum Qualifications:* Bachelor's Degree in a relevant technical field such as computer science or equivalent years of practical work experience* 5+ years of post-Bachelor's machine learning experience; or Master's degree in a technical field + 4+ year of post-grad machine learning experience; or PhD in a relevant technical field + 1 years of post-grad machine learning experience* Experience developing machine learning models for ranking, recommendations, search, content understanding, image generation, or other relevant applications of machine learning Preferred Qualifications:* Advanced degree in computer science or related field* Experience working with machine learning frameworks such as TensorFlow, Caffe2, PyTorch, Spark ML, scikit-learn, or related frameworks* Experience working with machine learning, ranking infrastructures, and system design If you have a disability or special need that requires accommodation, please don't be shy and provide us some ."Default Together" Policy at Snap: At Snap Inc. we believe that being together in person helps us build our culture faster, reinforce our values, and serve our community, customers and partners better through dynamic collaboration. To reflect this, we practice a “default together” approach and expect our team members to work in an office 4+ days per week. At Snap, we believe that having a team of diverse backgrounds and voices working together will enable us to create innovative products that improve the way people live and communicate. Snap is proud to be an equal opportunity employer, and committed to providing employment opportunities regardless of race, religious creed, color, national origin, ancestry, physical disability, mental disability, medical condition, genetic information, marital status, sex, gender, gender identity, gender expression, pregnancy, childbirth and breastfeeding, age, sexual orientation, military or veteran status, or any other protected classification, in accordance with applicable federal, state, and local laws. EOE, including disability/vets.We are an Equal Opportunity Employer and will consider qualified applicants with criminal histories in a manner consistent with applicable law (by example, the requirements of the San Francisco Fair Chance Ordinance and the Los Angeles Fair Chance Initiative for Hiring, where applicable).: Snap Inc. is its own community, so we've got your back! We do our best to make sure you and your loved ones have everything you need to be happy and healthy, on your own terms. Our benefits are built around your needs and include paid parental leave, comprehensive medical coverage, emotional and mental health support programs, and compensation packages that let you share in Snap's long-term success!CompensationIn the United States, work locations are assigned a pay zone which determines the salary range for the position. The successful candidate's starting pay will be determined based on job-related skills, experience, qualifications, work location, and market conditions. The starting pay may be negotiable within the salary range for the position.These pay zones may be modified in the future.:The base salary range for this position is $209,000-$313,000 annually.:The base salary range for this position is $199,000-$297,000 annually.:The base salary range for this position is $178,000-$266,000 annually.This position is eligible for equity in the form of RSUs. #J-18808-Ljbffr

Meet the Team Splunk, a Cisco company, is building a safer, more resilient digital world with an end‑to‑end, full‑stack platform designed for hybrid, multi‑cloud environments. The Splunk AI Platform and Services team provides the core runtime and developer experience that power AI across Splunk and Cisco. We manage large-scale, multi-tenant LLM inference across major cloud providers and build platform services to support these workloads. We also provide VectorDB/RAG services and MCP services that make AI workloads secure, observable, and cost-efficient for product teams. On top of this foundation, we deliver agentic frameworks, SDKs, tools, and evaluation/guardrail capabilities that help teams quickly build reliable GenAI assistants and automation features. You'll join a group that sits at the intersection of distributed systems, ML, and developer experience, grounded in operational excellence and a culture of impact‑driven, cross‑functional collaboration. Your Impact Implement features for GenAI services and APIs that power chat assistants, and automation workflows across Splunk products. Help build and maintain RAG pipelines: retrieval orchestration, hybrid search, chunking & embeddings, and grounding with logs/events/metrics. Contribute to agentic and multi‑agent workflows using frameworks like LangChain or LangGraph, integrating with MCP tools, internal APIs, and external systems. Develop and refine developer‑facing SDKs, templates, and reference apps (primarily Python/TypeScript) that make it simple for other teams to compose tools, chains, and agents on top of it. Integrate with LangSmith or similar eval stacks to instrument prompts, capture traces, and run evaluations under the guidance of more senior engineers and scientists. Collaborate with product managers and UX to turn user stories into GenAI experiences, iterate based on feedback, and ship features that move customer and business metrics. Apply and advocate responsible AI practices in day‑to‑day work: grounding, guardrails, access controls, and human‑in‑the‑loop flows. Minimum Qualifications: Bachelor's degree in computer science, Engineering, or equivalent practical experience. 5+ years of hands‑on experience building and operating backend or distributed systems in production or 2+ years of experience with a Master's degree Proficiency in at least one modern programming language (e.g., Python, TypeScript/JavaScript, Go, or Java) and solid software design/debugging skills. Some hands‑on experience with LLM APIs and ecosystems (e.g., OpenAI, Claude, Bedrock, or OSS models such as Llama) and related production features. Familiarity with web APIs and microservices (REST/gRPC), including testing, deployment, and basic observability (logs/metrics). Demonstrated ability to work end‑to‑end on features: collaborate on design, implement, write tests, help deploy, and iterate based on metrics or feedback. Preferred Qualifications: Experience or strong interest in RAG systems and vector databases (Weaviate, Qdrant, Milvus, FAISS, etc.). Exposure to agentic frameworks (LangChain, LangGraph, LlamaIndex, Semantic Kernel, or similar) and tool/agent orchestration patterns. Familiarity with LangSmith or similar evaluation platforms, or experience instrumenting prompts/pipelines for quality and debugging. Background contributing to platform or Developer experiences capabilities: internal libraries, SDKs, templates, or shared components that other engineers use. Experience with full‑stack development for GenAI interfaces (React/TypeScript), including prompt UX or conversation flows, is a plus. Understanding basic AI safety and governance concepts (guardrails, data privacy, RBAC) and how they apply in an enterprise environment. Strong communication skills and a growth mindset, comfortable asking questions, giving/receiving feedback, and learning from more senior teammates. Why Cisco? At Cisco, we're revolutionizing how data and infrastructure connect and protect organizations in the AI era - and beyond. We've been innovating fearlessly for 40 years to create solutions that power how humans and technology work together across the physical and digital worlds. These solutions provide customers with unparalleled security, visibility, and insights across the entire digital footprint. Fueled by the depth and breadth of our technology, we experiment and create meaningful solutions. Add to that our worldwide network of doers and experts, and you'll see that the opportunities to grow and build are limitless. We work as a team, collaborating with empathy to make really big things happen on a global scale. Because our solutions are everywhere, our impact is everywhere. We are Cisco, and our power starts with you. Message to applicants applying to work in the U.S. and/or Canada: The starting salary range posted for this position is $181,000.00 to $235,000.00 and reflects the projected salary range for new hires in this position in U.S. and/or Canada locations, not including incentive compensation*, equity, or benefits. Individual pay is determined by the candidate's hiring location, market conditions, job‑related skillset, experience, qualifications, education, certifications, and/or training. The full salary range for certain locations is listed below. For locations not listed below, the recruiter can share more details about compensation for the role in your location during the hiring process. U.S. employees are offered benefits, subject to Cisco's plan eligibility rules, which include medical, dental and vision insurance, a 401(k) plan with a Cisco matching contribution, paid parental leave, short and long‑term disability coverage, and basic life insurance. Please see the Cisco careers site to discover more benefits and perks. Employees may be eligible to receive grants of Cisco restricted stock units, which vest following continued employment with Cisco for defined periods of time. U.S. employees are eligible for paid time away as described below, subject to Cisco's policies: 10 paid holidays per full calendar year, plus 1 floating holiday for non‑exempt employees 1 paid day off for employee's birthday, paid year‑end holiday shutdown, and 4 paid days off for personal wellness determined by Cisco Non‑exempt employees** receive 16 days of paid vacation time per full calendar year, accrued at rate of 4.92 hours per pay period for full‑time employees Exempt employees participate in Cisco's flexible vacation time off program, which has no defined limit on how much vacation time eligible employees may use (subject to availability and some business limitations) 80 hours of sick time off provided on hire date and each January 1st thereafter, and up to 80 hours of unused sick time carried forward from one calendar year to the next Additional paid time away may be requested to deal with critical or emergency issues for family members Optional 10 paid days per full calendar year to volunteer For non‑sales roles, employees are also eligible to earn annual bonuses subject to Cisco's policies. Employees on sales plans earn performance‑based incentive pay on top of their base salary, which is split between quota and non‑quota components, subject to the applicable Cisco plan. For quota‑based incentive pay, Cisco typically pays as follows: .75% of incentive target for each 1% of revenue attainment up to 50% of quota; 1.5% of incentive target for each 1% of attainment between 50% and 75%; 1% of incentive target for each 1% of attainment between 75% and 100%; and Once performance exceeds 100% attainment, incentive rates are at or above 1% for each 1% of attainment with no cap on incentive compensation. For non‑quota‑based sales performance elements such as strategic sales objectives, Cisco may pay 0% up to 125% of target. Cisco sales plans do not have a minimum threshold of performance for sales incentive compensation to be paid. The applicable full salary ranges for this position, by specific state, are listed below: New York City Metro Area: $181,000.00 - $270,300.00 Non‑Metro New York state & Washington state: $165,300.00 - $240,600.00 For quota‑based sales roles on Cisco's sales plan, the ranges provided in this posting include base pay and sales target incentive compensation combined. ** Employees in Illinois, whether exempt or non‑exempt, will participate in a unique time off program to meet local requirements. Cisco is an affirmative action and equal opportunity employer and all qualified applicants will receive consideration for employment without regard to race, color, religion, gender, sexual orientation, national origin, genetic information, age, disability, veteran status, or any other legally protected basis. Cisco will consider for employment, on a case by case basis, qualified applicants with arrest and conviction records. #J-18808-Ljbffr

Join Two Dots to build a stronger financial system. Every time someone applies for a mortgage, car loan, or apartment lease, they submit financial documents that humans use to build a financial profile about them. The quality of these financial profiles is a key input that regulates the body temperature of the economy. Two Dots is building a better system to evaluate consumers consistently and fairly. We prevent fraud that humans can't see, and we surface value in atypical applications that would otherwise be discarded. Please note that we require all full-time employees to work from our office in San Francisco, CA. Role overview: Two Dots is looking for our 2nd Machine Learning Engineer, who will work closely with the CTO and the Staff ML Engineer. In this role you will design, develop, and deploy machine learning solutions, with a focus on fine tuning multimodal large language models (LLMs) to solve real-world problems. The ideal candidate will have a passion for building and deploying advanced ML applications, with the aim to produce business impact and client satisfaction by increasing our application approval/denial automation rate and increasing our fraud detection capabilities. Key Responsibilities: Work autonomously to design, develop and deploy machine learning models Analyze large datasets to uncover insights and trends that inform product development and personalized customer experiences Continuously monitor and improve the performance of deployed models, ensuring they meet business objectives and scalability requirements Stay up to date with the latest advancements in machine learning, AI, data science and engineering, and apply this knowledge to improve our products and services Desirable Traits: 3+ years of experience in a Machine Learning or Data Engineering role, with a strong proficiency in Python and ML frameworks like PyTorch required Proven ability to improve models for key information extraction, including named entity recognition and matching, and financial document classification Experience with active learning, HITL driven workflows; working with large labeling and quality teams is a plus Strong problem solving skills, with the ability to think critically and creatively Excellent communication and interpersonal skills, capable of explaining complex operational information in an understandable way A proactive, curious mindset with a relentless pursuit of excellence and innovation in tackling complex problems Hungry for personal and professional growth and ready to scale with Two Dots! What you get in return: An opportunity to revolutionize the real estate leasing industry and own projects that make a tangible impact An environment with a work culture that is based on trust, ownership, flexibility and a growth mindset A competitive salary, comprehensive equity package, and substantial benefits Closing: Two Dots is an equal opportunity employer. We aim to build a workforce of individuals from different backgrounds, with different abilities, identities, and mindsets. Even if you do not meet all of the qualifications listed above, we encourage you to apply! Compensation is variable and is subject to a candidate's personal qualifications and expectations. For this role, we offer the following base salary range (in addition to a large equity package and full benefits): $175k - $250+k per year. #J-18808-Ljbffr

At Pave, we're building the industry's leading compensation platform, combining the world's largest real-time compensation dataset with deep expertise in AI and machine learning. Our platform is perfecting the art and science of pay to give 8,500+ companies unparalleled confidence in every compensation decision. Top tier companies like OpenAI, McDonald's, Instacart, Atlassian, Synopsys, Stripe, Databricks, and Waymo use Pave, transforming every pay decision into a competitive advantage. $190+ billion in total compensation spend is managed in our workflows, and 58% of Forbes AI 50 use Pave to benchmark compensation. The future of pay is real-time & predictive, and we're making it happen right now. We've raised $160M in funding from leading investors like Andreessen Horowitz, Index Ventures, Y Combinator, Bessemer Venture Partners, and Craft Ventures. Research & Design Org Pave's R&D pillar includes our data science, engineering, information technology, product design, product management, and security teams. This organization builds, maintains, and secures a platform used by more than 8,500+ client organizations. Our engineering team moves between ideation, scoping, and execution in a matter of days while closely iterating with cross‑functional partners on requirements. At Pave, we use TypeScript, Node.js, and React, hosted on GCP. Compensation strategy is broken down into three pillars - compensation bands, planning workflows, and total rewards communication. We build products that make these processes seamless for customers. Over the next year, our roadmap is focused on enhancing the entire compensation lifecycle: from philosophy definition to market trend analysis, band adjustments, merit cycles, and employee communication. We're seeking passionate engineers who are excited about building robust, data‑rich systems that simplify complex compensation processes at scale. The Data Team @ Pave As part of the Data team at Pave, you will help us redefine how companies understand the labor market and determine compensation. Even the most innovative tech companies in the world often use spreadsheets full of flawed statistics to determine how to pay. At Pave, we've built a system of real‑time integrations that allow us to bring best practices from machine learning, data science, software tooling, and AI to an industry that is built on data, but doesn't have the tools it needs to fully leverage it. What You'll Do Architect and implement scalable ML systems for modeling compensation within a single company and across the market as a whole Collaborate with product and engineering teams to identify additional opportunities to leverage ML‑driven solutions Help evolve the technical direction of ML initiatives across the company Drive millions of dollars of revenue growth What You'll Bring 5+ years of experience building and deploying ML models in production environments Strong foundation in machine learning, statistics, and deep learning fundamentals Expertise in Python and modern ML frameworks (PyTorch, TensorFlow, or similar) Experience with large‑scale data processing and ML model optimization Experience with MLOps practices and tools (model versioning, monitoring, and deployment) Strong software engineering practices and experience with production systems Expert‑level SQL skills with experience writing complex queries and optimizing query performance Ability to navigate (and bring structure to) ambiguity; ability to bring a project from 0 to 1, or scale a project from 1 to 100 Compensation Salary is just one component of Pave's total compensation package for employees. Your total rewards package at Pave will include equity, top‑notch medical, dental, and vision coverage, a flexible PTO policy, and many other region‑specific benefits. Your level is based on our assessment of your interview performance and experience, which you can always ask the hiring manager about to understand in more detail. This salary range may include multiple levels. The targeted cash compensation for this position is (level depends on experience and performance in the interview process): P3: $195,000 - $215,000 P4: $230,000 - $250,000 Life @ Pave Since being founded in 2019, Pave has established a robust global footprint. Headquartered in San Francisco's Financial District, we operate strategic regional hubs across New York City's Flatiron District, Salt Lake City, and the United Kingdom. We cultivate a vibrant, collaborative workplace culture through our hybrid model, bringing teams together in‑person on Mondays, Tuesdays, Thursdays, and Fridays to foster innovation and strengthen professional relationships. Benefits Complete Health Coverage: Comprehensive Medical, Dental and Vision coverage for you and your family, with plenty of options to suit your needs Time off & Flexibility: Flexible PTO and the ability to work from anywhere in the world for a month Meals & Snacks: Lunch & dinner stipends as well as fully stocked kitchens to fuel you Professional Development: Quarterly education stipend to continuously grow Family Support: Robust parental leave to bond with your new family Commuter Assistance: A commuter stipend to help you collaborate in person Vision & Mission Our vision is to unlock a labor market built on trust. Our mission is to build confidence in every compensation decision. Equal Employment Opportunity As set forth in Pave's Equal Employment Opportunity policy, we do not discriminate on the basis of any protected group status under any applicable law. If you believe you belong to any of the categories of protected veterans listed below, we request this information in order to measure the effectiveness of the outreach and positive recruitment efforts we undertake pursuant to VEVRAA. #J-18808-Ljbffr

About Gusto About Gusto is a modern, online people platform that helps small businesses take care of their teams. On top of full-service payroll, Gusto offers health insurance, 401(k)s, expert HR, and team management tools. Today, Gusto offices in Denver, San Francisco, and New York serve more than 400,000 businesses nationwide. Our mission is to create a world where work empowers a better life, and it starts right here at Gusto. That's why we're committed to building a collaborative and inclusive workplace, both physically and virtually. Learn more about our Total Rewards philosophy. About the Role Gusto's Data Science team leverages Gusto's rich dataset to guide product direction and decision-making. We operate full-stack, conducting analyses, prototyping and deploying predictive models and statistical tools both for internal use and for our customers. For this role, we are looking for a technical leader (an individual contributor) to drive machine learning and AI in the payment and risk domains. You will build a model-driven risk platform to provide a trusted environment for Gusto Ecosystem. You'll be working with an established team and seasoned payments and risk leaders in Engineering, Product, Design, Operation, Identity and Compliance. In this role, you'll work cross functionally to build Platforms that span the entire breadth of the Payments and Risk Stacks, and use ML and AI to build a world- class, high secure platform that safeguards our users' activities and money, and ensures unparalleled reliability. Here's what you'll do day-to-day Build and deploy machine learning models to identify, assess and mitigate risks Responsible for driving research in the problem space, working with stakeholders to understand model requirements, developing the model from scratch, deploying the model alongside your engineering counterparts, and monitoring and maintaining the model's performance over time Partner with Engineering, Design, and Product counterparts in Payment and Risk to solve complex cross functional problems Develop scalable frameworks and libraries that enhance and contribute to the team's core analysis and modeling capabilities Identify new opportunities to leverage data to improve Gusto's products and help risk management team to understand business requirements and develop tailored solutions Present and communicate results to stakeholders across the company Here's what we\'re looking for 8+ years experience conducting statistical analyses on large datasets and deep domain knowledge in machine learning. Proven experience in the credit risk modeling or fraud risk modeling using logistic regression, random forest, Xgboost or neural networks Experience applying a variety of statistical and modeling techniques using Python, R or another statistical modeling language, as indicated by familiarity with many of the following techniques - predictive modeling, anomaly detection, ensemble methods, natural language processing (NLP, optional). Strong programming skills - comfortable with all phases of the data science development process, from initial analysis and model development all the way through to deployment Excellent communication skills - able to effectively deliver findings and recommendations to non-technical stakeholders in a clear and compelling fashion PhD or Masters plus equivalent experience in a quantitative field is a plus Experience in the Fintech industry is a plus Our cash compensation amount for this role is targeted at $170,000 - $205,000 in Denver, $185,000- $225,000 in Los Angeles, $205,000 - $250,000 for San Francisco, New York, and Seattle, and $185,000 - $225,000 CAD for Toronto, Canada. Final offer amounts are determined by multiple factors including candidate experience and expertise and may vary from the amounts listed above. Gusto has physical office spaces in Denver, San Francisco, and New York City. Employees who are based in those locations will be expected to work from the office on designated days approximately 2-3 days per week (or more depending on role). The same office expectations apply to all Symmetry roles, Gusto\'s subsidiary, whose physical office is in Scottsdale. Note: The San Francisco office expectations encompass both the San Francisco and San Jose metro areas. When approved to work from a location other than a Gusto office, a secure, reliable, and consistent internet connection is required. Our customers come from all walks of life and so do we. We hire great people from a wide variety of backgrounds, not just because it\'s the right thing to do, but because it makes our company stronger. If you share our values and our enthusiasm for small businesses, you will find a home at Gusto. Gusto is proud to be an equal opportunity employer. We do not discriminate in hiring or any employment decision based on race, color, religion, national origin, age, sex (including pregnancy, childbirth, or related medical conditions), marital status, ancestry, physical or mental disability, genetic information, veteran status, gender identity or expression, sexual orientation, or other applicable legally protected characteristic. Gusto considers qualified applicants with criminal histories, consistent with applicable federal, state and local law. Gusto is also committed to providing reasonable accommodations for qualified individuals with disabilities and disabled veterans in our job application procedures. If you require a medical or religious accommodation at any time throughout your candidate journey, please fill out this form and a member of our team will get in touch with you. Gusto takes security and protection of your personal information very seriously. Please review our Fraudulent Activity Disclaimer. Personal information collected and processed as part of your Gusto application will be subject to Gusto\'s Applicant Privacy Notice. #J-18808-Ljbffr

Tensor is an agentic AI company dedicated to building agentic products that empower individual consumers. Our flagship product, the Tensor Robocar, is the world's first personal Robocar and the first AI agentic vehicle - fully autonomous, automotive-grade, and built for private ownership at scale. Founded in 2016 in Silicon Valley, Tensor is headquartered in San Jose, California, with offices in Barcelona, Singapore, and Dubai. At Tensor, we champion personal AI autonomy and ownership. Our vision is to build a future where everyone owns their own Artificial General Intelligence - a personal AGI that enables more time, freedom, and autonomy. We're forging an alternative path where AGI serves only you, and is controlled solely by you. We provide a competitive compensation package, opportunities for professional growth, participation in a discretionary equity incentive plan, and access to a comprehensive company benefits program, subject to eligibility requirements. Join us to shape the future! Work with brilliant minds on technology transforming automotive industry and make a lasting impact. Locations San Jose, California, US (Salary Range: $75k-$300k USD) Barcelona, Catalonia, Spain Dubai, UAE Responsibilities Research, design and implement novel computer vision and deep learning methods on autonomous driving lidar/camera data Solve difficult problems independently and creatively Communicate effectively with team members and stakeholders Qualifications MS/Ph.D. degree in Computer Science/Robotics/Mechanical Engineering/ Electrical Engineering, or related technical field(or equivalent experience) Strong curiosity about tackling and solving new real-world problems Strong background in Computer Vision and Machine Learning Solid programming skills with c++ and python/torch Experience with points cloud deep learning project is a big plus We appreciate your interest in joining Tensor. Tensor is an equal employment opportunity employer, dedicated to fostering a respectful, supportive, and inclusive workplace for all. We do not discriminate against, and strictly prohibit harassment of, any applicant or employee on the basis of race, color, sex, sexual orientation, gender identity or expression, religion, national origin, age, disability, military or veteran status, genetic information, or any other characteristic protected by applicable law. Tensor also considers qualified applicants with criminal histories in accordance with applicable laws. We are committed to providing equal opportunities for qualified individuals with disabilities. Tensor is an equal opportunity employer and value diversity at our company. We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status. Designed in California. Available around the world. #J-18808-Ljbffr

Palo Alto, CA - Engineering - Hybrid - Full-time Building hardware is like writing software with no debugger, no logs, and only three compile attempts - before mass production. This lack of visibility leads to costly waste and slow iteration. Instrumental's AI-powered platform changes that current reality. We give hardware teams real-time data and insights to catch and fix issues early-so they can build better products, faster, and with less waste. Leading brands like Meta, Bose, and Cisco use Instrumental to accelerate product development and improve yield at scale. We're a ~70-person, mission-driven team that values inclusivity, creativity, and impact. If that resonates with you, we'd love to chat! About The Role: We're looking for a Machine Learning Engineer who's passionate about turning cutting‑edge research into customer impact. You'll help build and scale Instrumental's end‑to‑end ML systems - from prototyping to production - enabling our customers to identify and solve manufacturing issues faster than ever. At Instrumental, ML engineers don't just train models - they own the problem. You'll have full lifecycle ownership: partnering with product and data teams to define features, exploring state‑of‑the‑art methods, curating datasets, iterating rapidly, deploying at scale, and monitoring real‑world performance. Your work will directly shape the product experience for some of the world's most innovative hardware companies. If you want meaningful ownership, tangible impact, and a collaborative environment that values learning and execution - this is the place. What You'll Do: Design, build, and own ML pipelines end‑to‑end-from experimentation to deployment and impact measurement. Collaborate across R&D to deliver holistic solutions that combine ML, software, and user experience. Rapidly prototype algorithms and prioritize work based on real customer needs. Scale production ML systems using modern frameworks and best practices. Lead efforts to acquire, manage, and refine high‑quality datasets for supervised and unsupervised learning. What You'll Need To Be Successful: Experience in writing production code with a focus on maintainability and performance. Experience training deep learning models, including expertise in model selection, training, optimization, and deployment. You have startup DNA: a growth mindset, a bias toward action, and a drive to take ownership of challenging projects with minimal guidance. You're resourceful - when you hit a wall, you find a way around it, learn what you need, and keep moving forward. Computer vision expertise (or a strong foundation in deep learning from other domains, such as NLP). If you don't have direct computer vision experience, a willingness to apply your deep learning knowledge to this area is important. Nice to have: Experience with cloud infrastructure, such as AWS, GCP, or Azure, and familiarity with scaling machine learning models in a cloud‑based environment. We're a growing team that works collaboratively, is supportive of each other, and is highly energized by the opportunity for a large impact. We actively work to promote an inclusive environment, valuing passion and the ability to learn. You're encouraged to apply even if your experience doesn't precisely match the job description! We're additionally open to multiple levels - if you're a Senior, Staff, or Principal‑level engineer, we'd also love to hear from you! Salary range: $168,000 - $270,000 Instrumental is proud to offer a highly‑rated variety of benefits, including health, vision, dental, commuter plans, and parental leave. #J-18808-Ljbffr

About Pinterest: Millions of people around the world come to our platform to find creative ideas, dream about new possibilities and plan for memories that will last a lifetime. At Pinterest, we're on a mission to bring everyone the inspiration to create a life they love, and that starts with the people behind the product. Discover a career where you ignite innovation for millions, transform passion into growth opportunities, celebrate each other's unique experiences and embrace the flexibility to do your best work. Creating a career you love? It's Possible. We are looking for a Principal Machine Learning Engineer, a senior technical visionary, to be the Principal Technical Lead for the Growth Engineering team, responsible for setting up overall technical strategy, unified technical architecture and defining a roadmap for industry leading methodology for user and engagement growth. Strong hands on machine learning background including deep learning architectures, generative AI, and large scale deployment and measurement of ML systems is required. What you'll do Develop strong partnerships with product teams to understand and proactively address future technology needs and current developer pain points. Champion and drive large-scale, cross-functional initiatives that grow user visitation and engagement depth of our platform. Act as the ultimate “advocate” for engineers on Growth including representing needs to leadership and prioritizing projects on the platform teams that ensure high quality capabilities and a world-class Pinner experience. Scale your leadership through both direct mentorship and via best practices, processes, training and tools. Ensure solid technical plans are in place for projects within Growth via direct review or delegation. Be the technical point of contact for decisions that impact the whole Pinterest platform via the Growth initiatives and for cross-functional partners for an 125+ member org. What we're looking for Deep expertise building large scale ML systems at scale with modern frameworks. Knowledge of (and a passion for) building responsible and quality first discovery surfaces to drive user visitations. Track record of innovating and delivering large, cross-functional projects across multiple organizations. Strong written and verbal communication skills and proven ability to collaborate cross-functionally. Degree in Computer Science, Machine Learning, Statistics or related field. 10+ years of professional experience as a hands-on engineer and technical leader leading multiple projects. In-Office Requirement We let the type of work you do guide the collaboration style. That means we're not always working in an office, but we continue to gather for key moments of collaboration and connection. This role will need to be in the office for in-person collaboration 1-2 times every 6-months and therefore can be situated anywhere in the country. Relocation This position is not eligible for relocation assistance. Visit our PinFlex page to learn more about our working model. US based applicants only Salary range: $267,393-$550,515 USD Information regarding the culture at Pinterest and benefits available for this position can be found here. Our Commitment to Inclusion Pinterest is an equal opportunity employer and makes employment decisions on the basis of merit. We want to have the best qualified people in every job. All qualified applicants will receive consideration for employment without regard to race, color, ancestry, national origin, religion or religious creed, sex (including pregnancy, childbirth, or related medical conditions), sexual orientation, gender, gender identity, gender expression, age, marital status, status as a protected veteran, physical or mental disability, medical condition, genetic information or characteristics (or those of a family member) or any other consideration made unlawful by applicable federal, state or local laws. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements. If you require a medical or religious accommodation during the job application process, please complete this form for support. #J-18808-Ljbffr

About Arc Institute The Arc Institute is a new scientific institution that conducts curiosity-driven basic science and technology development to understand and treat complex human diseases. Headquartered in Palo Alto, California, Arc is an independent research organization founded on the belief that many important research programs will be enabled by new institutional models. Arc operates in partnership with Stanford University, UCSF, and UC Berkeley. While the prevailing university research model has yielded many tremendous successes, we believe in the importance of institutional experimentation as a way to make progress. These include: Funding: Arc will fully fund Core Investigators' (PIs') research groups, liberating scientists from the typical constraints of project-based external grants. Technology: Biomedical research has become increasingly dependent on complex tooling. Arc Technology Centers develop, optimize and deploy rapidly advancing experimental and computational technologies in collaboration with Core Investigators. Support: Arc aims to provide first-class support-operationally, financially and scientifically-that will enable scientists to pursue long-term high risk, high reward research that can meaningfully advance progress in disease cures, including neurodegeneration, cancer, and immune dysfunction. Culture: We believe that culture matters enormously in science and that excellence is difficult to sustain. We aim to create a culture that is focused on scientific curiosity, a deep commitment to truth, broad ambition, and selfless collaboration. Arc has scaled to nearly 200 people. With $650M+ in committed funding and a state‑of‑the‑art new lab facility in Palo Alto, Arc will continue to grow quickly in the coming years. About the position We are searching for an experienced and collaborative machine learning research engineer focused on advancing the frontiers of biological foundation models. This role will contribute to the development and application of Arc's frontier DNA foundation model (Evo), Arc's Virtual Cell Initiative focused on developing cell biological models capable of predicting the impact of perturbations and stimuli, and other projects in the context of Institute‑wide machine‑learning efforts. About you You are an innovative machine learning engineer with a deep understanding of ML principles, enabling you to design, modify, and critically evaluate model architectures, not just apply existing ones. You have significant experience in training large deep learning models. You enjoy thinking from first principles, seeking to deeply understand the data and its underlying dynamics to drive effective and innovative modeling strategies. You are excited about working closely with a multidisciplinary team of computational and experimental biologists at Arc to achieve breakthrough capabilities in biological prediction and design tasks. You are a strong communicator, capable of translating complex technical concepts to researchers outside of your domain. You are a continuous learner and are enthusiastic about developing and evaluating a model that impacts many biological disciplines. In this position, you will Actively participate in the design, implementation, and refinement of state‑of‑the‑art foundation models developed in collaboration with other ML researchers and scientists at Arc with the goal of understanding and designing complex biological systems. Engineer large‑scale distributed model pretraining and pipelines for efficient model inference. Enable robust systematic evaluation of trained models. Stay up‑to‑date with the latest advancements in technologies for large‑scale sequence modeling and alignment, and implement the most promising strategies to ensure the underlying models remain state‑of‑the‑art. Work with experimental biologists to ensure that the developed models are grounded in biologically meaningful problems and evaluations. Publish findings through journal publications, white papers, and presentations (both internal to Arc and external). Foster internal and external collaborations centered on generative design of biological systems at Arc Institute. Commit to a collaborative and inclusive team environment, sharing expertise and mentoring others. Job Requirements B.S., M.S. or Ph.D. in Computer Science, Machine Learning or a related field. Minimum of 5‑8+ years of relevant experience in machine‑learning research or ML engineering in an academic (e.g., Ph.D.) or industry research lab. Well‑versed in machine‑learning frameworks such as PyTorch or JAX. Experience with developing distributed training tools such as FSDP, DeepSpeed, or Megatron‑LM. Excellent communication skills, both written and verbal, with a strong track record of presentations and publications. Ability to communicate and collaborate successfully with biologists and software/infrastructure engineers. Motivated to work in a fast‑paced, ambitious, multi‑disciplinary, and highly collaborative research environment. The base salary range for this position is $168,000‑$242,500. These amounts reflect the range of base salary that the Institute reasonably would expect to pay a new hire or internal candidate for this position. The actual base compensation paid to any individual for this position may vary depending on factors such as experience, market conditions, education/training, skill level, and whether the compensation is internally equitable, and does not include bonuses, commissions, differential pay, other forms of compensation, or benefits. This position is also eligible to receive an annual discretionary bonus, with the amount dependent on individual and institute performance factors. #J-18808-Ljbffr

Newark, DE, United States Santa Clara, CA, United States United States As a Principal Machine Learning Engineer in Hologic's Breast and Skeletal Health division, you will play a pivotal role in designing, developing, and deploying advanced AI algorithms for next‑generation medical devices. You will focus on creating and validating AI‑driven solutions for breast cancer detection in breast tomosynthesis (3D mammography). Your work will have a direct impact on patient outcomes by ensuring our technologies are robust, safe, and clinically validated-upholding Hologic's mission to deliver innovation with precision and reliability. What You'll Bring Knowledge Deep expertise in machine learning and deep learning, including supervised and self‑supervised methods. Mastery of cutting‑edge neural network architectures and training techniques. Strong foundation in computer vision, data preprocessing, feature engineering, and statistical analysis. Experience with model validation and performance benchmarking. Knowledge of software engineering best practices for building maintainable, scalable systems. Bonus: Familiarity with FDA regulatory standards for AI in healthcare, DICOM format, digital breast tomosynthesis, and breast cancer pathology/diagnostic workflows. Skills Advanced programming in Python and C++, with proficiency in ML/data science libraries (e.g., Pandas, OpenCV, XGBoost, NumPy, SciPy). Hands‑on experience with deep learning frameworks (PyTorch, TensorFlow) and cloud platforms (AWS, Azure, GCP). Proven ability to design, implement, and optimize machine learning pipelines for large‑scale, high‑dimensional 3D medical imaging data. Experience deploying and maintaining models in production environments. Strong problem‑solving skills for translating clinical requirements into technical solutions. Excellent communication skills for technical documentation and stakeholder presentations. Effective cross‑functional collaboration with engineers, clinicians, and product managers. Behaviors Demonstrates leadership and initiative in driving complex projects from concept to deployment. Champions a collaborative, open, and success‑driven team culture. Maintains high standards of integrity, accountability, and ethical decision‑making. Adapts quickly to evolving technologies and priorities. Proactively seeks feedback and growth opportunities. Shows resilience and resourcefulness in technical and regulatory challenges. Prioritizes patient safety, data privacy, and compliance. Inspires teams through commitment to Hologic's mission and values. So why join Hologic? We are committed to making Hologic the company where top talent comes to grow. For you to succeed, we want to enable you with the tools and knowledge required and so we provide comprehensive training when you join as well as continued development and training throughout your career. We offer a competitive salary and annual bonus scheme, one of our talent partners can discuss this in more detail with you. If you have the right skills and experience and want to join our team, apply today. We can't wait to hear from you! The annualized base salary range for this role is $126,000 - $210,100 and is bonus eligible. Final compensation packages will ultimately depend on factors including relevant experience, skillset, knowledge, geography, education, business needs and market demand. Agency and Third‑Party Recruiter Notice: Agencies that submit a resume to Hologic must have a current executed Hologic Agency Agreement executed by a member of the Human Resource Department. In addition Agencies may only submit candidates to positions for which they have been invited to do so by a Hologic Recruiter. All resumes must be sent to the Hologic Recruiter under these terms or they will not be considered. As part of our commitment to a fair and accurate evaluation of each candidate's qualifications, we require all applicants to refrain from using AI tools, such as generative AI or automated writing assistance, during any stage of the interview process. Responses influenced by AI may result in disqualification. We appreciate your understanding and cooperation in ensuring a transparent and equitable selection process. Hologic, Inc. is proud to be an Equal Opportunity Employer inclusive of disability and veterans. LI-#DS1 #J-18808-Ljbffr

About the role Your impact Onyx is a popular open source project with hundreds of thousands of users. The project has over 10K stars and over 3K community members across Slack and Discord (these stats may already be out of date when you read this). You'll have the opportunity to build in the open and your work may be used by millions of people in the future. About the role Onyx is the knowledge layer on top of LLMs. Help us improve our agent and knowledge retrieval capabilities to push the frontier on unsolved problems like multi-hop QA, needle in haystack, aggregation type RAG, etc. This is an in-person role based in San Francisco, CA. You'll be: Evaluating and implementing LLM based knowledge graphs, advanced RAG approaches (StructRAG, etc.), LLM agents, advances in NLP, multi-modal transformers, advanced information retrieval algorithms Working on users' experience with the platform through features like learn from feedback, search personalization, SME suggestion, etc. Build a semantic and programmatically useful understanding of the organization's priorities, projects, and people as additional signals to the answering capabilities of Onyx Own the approach from inception to validation to production code Collaborate with Founders and the Head of AI to shape and influence the direction of the product and contribute to the AI/ML engineering strategy You'll be successful if you… Have 3+ years of AI/ML engineering experience building real-world applications have in-depth experience with PyTorch/Tensorflow, NLP models, and standard ML algorithms Are up date with new advances such as open source/proprietary LLMs, RAG and agent-frameworks Strong software engineering background and capable of building backend features with web frameworks, ORMs and relational DBs Excellent communication skills and ability to collaborate with full stack roles Bonus points Familiar with the full stack Typescript/React/NextJS, Python, Postgres Interested in writing technical blogs to establish Onyx is leader in the space About the interview Non-technical Phone Screen (30 mins) ML Interview (45 mins) Practical Coding Interview (30 mins) Work Trial (3 days in person, fully covered + compensated) About Onyx Onyx is the open source GenAI platform connected to your company's docs, apps, and people. We ingest and sync from all sources of information (Google Drive, Slack, GitHub, Confluence, Salesforce, etc.) to provide a centralized place for users to ask about anything. Imagine your most knowledgeable co-workers, all-rolled into one, and available 24/7! We believe that every modern team will be adopting knowledge enhanced GenAI within the next 5 years and it is our goal to bring this technology to all the teams of the world. We raised a $10M seed coming out of YCombinator, backed by Khosla Ventures (early/seed backers of OpenAI, Doordash, GitLab, etc.) and First Round Capital (Notion, Square, Roblox, etc.). Our customers include of the best teams in the world like Netflix, Ramp, Applied Intuition and dozens of others. We also have incredible open source users like Roku, Zendesk, L3Harris and more. #J-18808-Ljbffr

About the role: LangChain is hiring an Education Engineer to help developers and agent builders learn how to build, evaluate, and continuously refine agents using LangSmith. In this role, you'll turn cutting‑edge applied AI techniques into accessible, engaging educational content across multiple formats - from online courses to in‑person meetups and workshops. You will collaborate with our Applied AI and engineering teams to create high‑quality learning experiences that explain core concepts in agentic AI evaluation, monitoring, and iterative refinement. You will guide developers through real‑world examples, showcase (and develop) best practices, and help the community succeed with LangChain tools. This is a hybrid role that blends technical machine learning experience with a passion for education, community building, and communication. LangChain is uniquely positioned in the industry with the leading Agent Tracing, Evaluation, and Monitoring platform paired with the most vibrant agent developer community. This tight coupling enables developers to overcome the most significant hurdle in deploying agents today: reliability. We need an educator who can understand and communicate these advantages to the community. What you'll do: Collaborate with LangChain engineers to develop educational content that teaches agentic evaluation, monitoring and refinement using LangSmith, LangChain and LangGraph. Design curriculum and structured learning paths for our community of over 1 million developers and agent builders. Create and deliver content across multiple formats: Online courses for LangChain Academy, video tutorials, and webinars Live presentations at workshops, hackathons, meetups, and conferences Build and maintain example projects, code demos, and visuals to support educational content. Translate experimental applied AI code and internal agent evaluation techniques into crisp, developer-friendly learning materials. How to be successful in the role: A technical background and domain expertise in applied AI (Machine Learning, LLMs etc). You should be comfortable using datasets to run experiments, analyze model performance, and iterate toward more reliable, higher-quality outcomes. 2+ years experience as a software engineer/developer who enjoys and excels at making technical concepts understandable. Previous experience developing online asynchronous curriculum in the areas of GenAI, AI, machine learning, data science, robotics, or similar. Strong working knowledge of generative AI concepts, agents, and agent evaluation. A clear, engaging communication style - both written and on camera. Experience designing and teaching technical content for developers (courses, workshops, or technical onboarding). Familiarity with LangChain, LangSmith or similar LLM frameworks/tools is a strong plus. A degree in Computer Science, Machine Learning, Math, Data Science, and/or equivalent industry experience. Compensation and Benefits: We offer competitive compensation including base salary, equity, and benefits such as health and dental coverage, flexible vacation, a 401(k) plan, and life insurance. Compensation varies based on role, level, and location. Annual salary range: $155,000 - 185,000 #J-18808-Ljbffr

About Remedy Robotics Cardiovascular disease is the #1 cause of morbidity and mortality in the world. Much of this could be prevented with better access to specialist care. Take stroke as an example: any delay in treatment can lead to permanent disability or death. However, due to a lack of specialist surgeons, the most effective intervention can only be performed in 2% of US hospitals. For patients who present to one of the 98% of hospitals that do not offer the surgery, treatment is either significantly delayed or not offered at all because timely transfer is not feasible. Our mission is to bring state-of-the-art vascular intervention to anyone, anytime, regardless of their location. Our team of medical clinicians, roboticists, and machine learning experts are working to bridge this gap by building the world's first remotely-operated, semi-autonomous endovascular surgical robot. We've already done what nobody else could-using our system, doctors from around the world were able to remotely perform this procedure from as far as 8000 miles away. We have now successfully performed first-in-human cases, including a remotely operated procedure, demonstrating the potential of our technology to revolutionize access to life-saving interventions. We now need your help to bring this technology out of the laboratory and into hospitals everywhere. The Role We're looking for someone to continue leveraging our vast trove of medical imaging data in order to train and deploy deep neural network models. These models enable our surgical robot to understand and reason about both our robot and the patient's anatomy, which ultimately gives doctors the insight and control necessary to quickly and safely complete the procedure. You Have At least 2 years of machine learning engineering experience (level will be commensurate with your experience) Experience developing high-quality software, ranging from design and implementation to testing and deployment Expertise with Python Experience training image-based deep neural networks, including Deep neural network libraries such as PyTorch Defining training and validation datasets Using data augmentations during training Selecting loss functions and metrics Cloud-based data and training Conducting large-scale experiments to determine actionable improvements Eagerness to learn on the job, iterate fast, and collaborate Nice to Haves Experience developing and deploying neural networks for physical systems, such as robots and autonomous vehicles Experience with medical imaging data such as x-rays, CTs, and MRIs Experience bridging the sim-to-real gap #J-18808-Ljbffr

We are searching for a Machine Learning Engineer to help us build upon the intelligence and capabilities of our robots and the machine learning systems that power them. Our products combine state-of-the-art perception algorithms and adaptive decision-making abilities with sophisticated control algorithms to endow industrial robots with the autonomy needed to perform efficiently at scale in several industrial applications. As a Machine Learning Engineer, you will work closely with our robotics, deployment, and sales teams to identify customer needs and conceptualize and build out machine learning solutions to those needs. You will also be responsible for monitoring, training, and validating performance of our deep learning models. We value candidates who are passionate about what they build, feel a strong sense of ownership over their work, and love being continually challenged. Our ideal candidates are those who care deeply about their team and demonstrate both directness and integrity. Examples of the work you will be doing Building product features, data pipelines, and tooling for visualizing and understanding machine learning models Creating and adapting SOTA model architectures in Tensorflow or PyTorch to solve unique, high-value problems Applying best practices in data science to help the team draw actionable conclusions from our data and performance results Working with our machine learning team to automate, manage, and validate our data collection pipelines and processes Collecting, organizing, and presenting performance results to stakeholders Technologies you will work with Python Rust Google Cloud Platform (BigQuery, Cloud Spanner, & Cloud Storage) Airflow Kubernetes Terraform gRPC Tensorflow CI/CD Qualifications B.S. or higher degree in computer science or related engineering field Excellent Python programming skills Experience with Tensorflow or a similar deep learning framework Knowledge of key concepts in machine learning (particularly deep learning) and data science Familiarity with big data and databases (BigQuery) Strong ethic of self-reliance and the initiative to take ownership of projects Strong communication skills Ability to determine reasonable timelines and deliver results accordingly Legal authorization to work in the U.S. is required Nice to haves Strong theoretical knowledge and practical experience with classical machine learning and deep learning techniques Experience with 3D scene understanding and object detection/segmentation from vision Experience with robotic manipulation hardware: arms, 3D cameras, force sensors Experience working independently on large, shared code bases and infrastructure Experience with big data processing tools: Spark/Hive/BigQuery etc. More About OSARO OSARO is a San Francisco-based startup company applying deep learning technology to next-generation robotics applications. Some of Silicon Valley's leading investors, including Peter Thiel, Jerry Yang, and Scott Banister have backed OSARO. Our vision is to build brains for robots on an industrial scale and we are excited and driven to see the results of our efforts operating in and interacting with the real world. We implement state-of-the-art techniques but constantly strive to build the simplest possible solution. OSARO is technique agnostic and always focused on the goal. We regularly review academic literature and techniques while steering clear of the hype. We're focused on delighting our customers with systems that work like magic. We have a highly international team made up of expert machine learning practitioners and dedicated software and hardware engineers which match well with the global nature of our business. We are naturally curious, love healthy debate, and respect varying points of view. At OSARO, we strive to be champions for equality. We believe we can serve as a model for diversity in the tech industry by emphasizing policies of nondiscrimination and inclusion at every step. We are an equal opportunity employer who offers Health, dental, vision, and commuter benefits & stipend Catered lunches in a dog friendly office, phone and learning stipend Generous vacation time Excellent paid parental leave policy with the option for additional reduced and unpaid leave The above full-time position is available immediately. You should be willing to move to the SF Bay Area and physically be in the office. This is not a remote position. 140000 - 180000 USD a year Actual compensation is based on various factors, including but not limited to job-related skills, and experience. #J-18808-Ljbffr