Machining engineer jobs in Fremont, CA - 12,818 jobs

Strava is the app for active people. With over 150 million athletes in more than 185 countries, Strava is where connection, motivation, and personal bests thrive. No matter your activity, gear, or goals, we help you find your crew, crush your milestones, and keep moving forward. Start your journey with Strava today. Our mission is simple: to motivate people to live their best active lives. We believe in the power of movement to connect and drive people forward. About This Role We are looking for a Senior Machine Learning Platform Engineer to join the growing AI and Machine Learning team at Strava. This team is responsible for developing sophisticated machine learning models and systems, plus leveraging generative AI technologies. Together this provides value to Strava athletes in various aspects including personalization, recommendations, search, and trust and safety. This is an important role on the team to develop and expand the platform behind the curtain. This lets us build models of higher quality with less friction. It helps ensure our models are served with stability and reliability, while ensuring we monitor model performance carefully. Ultimately you won't just help with the things we are doing now, but also unlock our technological capabilities for the future. We follow a flexible hybrid model that translates to more than half your time on-site in our San Francisco office- three days per week. What You'll Do: Own End to End Systems: Drive key projects to power AI/ML at Strava end-to-end from gathering stakeholders requirements to ground up developer to driving adoption and optimizing the experience Interact with a Rich and Large Dataset: Explore and help leverage Strava's extensive unique fitness and geo datasets from millions of users to extract actionable insights, inform product decisions, and optimize existing features Contribute to a Well Loved Consumer Product: Work at the intersection of AI and fitness to help launch and maintain product experiences that will be used by tens of millions of active people worldwide You Will Be Successful Here By: Holding empathy and perspective: Work closely with engineers and data scientists to understand the opportunities to help them succeed; they will be your customers! Leading as an owner: Owning your work end-to-end and being accountable for the outcomes in the projects you lead, influencing the ML team, partner teams, and landing impact for the business. Ensure the end-to-end system delivers as expected through collaboration with partners. Collaborating in and across teams: Build relationships, advocate and communicate with cross-functional partners and product verticals to identify opportunities and bring your technical vision to life. Driving innovation with product in mind: Stay up-to-date with the latest research in machine learning, AI, and related fields. Experiment, advocate, and gain buy-in for innovative techniques to enhance our existing platform, resulting in step-function changes to how we build AI at Strava. Being passionate about the work you are doing and contributing positively to Strava's inclusive and collaborative team culture and values What You'll Bring to the Team: Have worked on complex, ambiguous platform challenges and broken them down into manageable tasks with both strategies and tactical execution. Demonstrated technical leadership in leading projects and the ability to mentor and grow early-career team members. Have demonstrated strong interpersonal and communication skills, and a collaborative approach to drive business impact across teams. Have worked with a variety of MLOps tools that fulfill different ML needs (like FastAPI, LitServe, Metaflow, MLflow, Kubeflow, Feast) Are experienced in production ML model operational excellence and best practices, like automated model retraining, performance monitoring, feature logging, A/B testing Experience with generative AI technologies around LLM evaluation, vector stores, and agent frameworks. Have built backend production tools and services on cloud environments like (but not limited to) AWS, using languages Python, Terraform, and other similar technologies. Have built and worked on data pipelines using large scale data technologies (like Spark, SQL, Snowflake) Have experience building, shipping, and supporting ML models in production at scale Have experience with exploratory data analysis and model prototyping, using languages such as Python or R and tools like Scikit learn, Pandas, Numpy, Pytorch, Tensorflow, Sagemaker For more information on benefits, please click here. Why Join Us? Movement brings us together. At Strava, we're building the world's largest community of active people, helping them stay motivated and achieve their goals. Our global team is passionate about making movement fun, meaningful, and accessible to everyone. Whether you're shaping the technology, growing our community, or driving innovation, your work at Strava makes an impact. When you join Strava, you're not just joining a company-you're joining a movement. If you're ready to bring your energy, ideas, and drive, let's build something incredible together. Strava builds software that makes the best part of our athletes' days even better. Just as we're deeply committed to unlocking their potential, we're dedicated to providing a world-class, inclusive workplace where our employees can grow and thrive, too. We're backed by Sequoia Capital, TCV, Madrone Partners and Jackson Square Ventures, and we're expanding in order to exceed the needs of our growing community of global athletes. Our culture reflects our community. We are continuously striving to hire and engage teammates from all backgrounds, experiences and perspectives because we know we are a stronger team together. Strava is an equal opportunity employer. In keeping with the values of Strava, we make all employment decisions including hiring, evaluation, termination, promotional and training opportunities, without regard to race, religion, color, sex, age, national origin, ancestry, sexual orientation, physical handicap, mental disability, medical condition, disability, gender or identity or expression, pregnancy or pregnancy-related condition, marital status, height and/or weight. We will ensure that individuals with disabilities are provided reasonable accommodation to participate in the job application or interview process, to perform essential job functions, and to receive other benefits and privileges of employment. Please contact us to request accommodation. #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

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

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

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

Please note this posting is to advertise potential job opportunities. This exact role may not be open today but could open in the near future. When you apply, a Cisco representative may contact you directly if a relevant position opens. Applications are accepted until further notice. Meet the Team Join our innovative engineering team focused on building next-generation AI/ML solutions. You'll collaborate with skilled colleagues across platform, security, release engineering, and support teams to deliver high-impact products and ensure their perfect operation. Your Impact Dive into the development and implementation of cutting‑edge generative AI applications using the latest large language models-think GPT‑4, Claude, Llama, and beyond! Take on the challenge of optimizing neural networks for natural language processing and machine perception, drawing on a toolkit that includes convolutional and transformer‑based models, student‑teacher frameworks, distillation, and generative adversarial networks (GANs). Performance, scalability, and reliability are front and center as models are trained, fine‑tuned, and put through their paces for real‑world deployment. Collaboration is at the heart of this role-work alongside talented engineers and cross‑functional teams to gather and prep data, design custom layers, and automate model deployment. Experimentation with new technologies and ongoing learning are always encouraged. Production‑ready code, robust testing, and creative problem‑solving all play a part in bringing innovative AI solutions to life. What an exciting place to grow and make an impact! Minimum Qualifications Recent graduate or in your final year of toward a PhD in Computer Science, Electrical Engineering, Artificial Intelligence, Machine Learning, or a related field. 3+ years of experience in backend development using Go or Python. Understanding of LLM infrastructure and optimization, validated by technical interview responses, project documentation, or relevant publications. Hands‑on experience with model building and AI/LLM research, demonstrated through portfolio work, code samples, technical assessments, or documented academic or professional projects. Preferred Qualifications Experience working with inference engines (e.g., vLLM, Triton, TorchServe). Knowledge of GPU architecture and optimization. Familiarity with agent frameworks. Exposure to cloud native solutions and platforms. Experience with cybersecurity principles and Python programming, including common AI libraries. Familiarity with distributed systems and asynchronous programming models. 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: 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 Affir mative 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

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

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

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 Bland At Bland.com, our goal is to empower enterprises to make AI-phone agents at scale. Based out of San Francisco, we're a quickly growing team striving to change the way customers interact with businesses. We've raised $65 million from Silicon Valley's finest; Including Emergence Capital, Scale Venture Partners, YC, the founders of Twilio, Affirm, ElevenLabs, and many more. About the Role As a Senior ML Engineer at Bland, you'll own the intelligence behind our voice AI platform. You're not just optimizing models-you're architecting the ML systems that make our agents sound genuinely human and drive real business outcomes for enterprise customers. Your work directly impacts whether our agents can handle complex, nuanced conversations or sound like corporate robots. What You'll Do Own the full ML stack: Lead engineering and optimization efforts for our self-hosted STT, LLM, and TTS systems from research through production deployment. Build production-grade inference systems: Design and implement high-throughput ML infrastructure serving millions of daily voice interactions with sub-second latency requirements. Drive model performance: Research and implement novel approaches to improve our models' conversational quality, RAG pipelines, and reduce latency, Optimize for enterprise scale: Handle complex inference optimization challenges-model quantization, efficient serving architectures, and cost optimization for large-scale deployments. Collaborate across teams: Work closely with Deployment Engineers to understand customer requirements and translate business needs into ML solutions that actually work in production. Push the boundaries: Experiment with cutting-edge techniques in conversational AI, real-time speech processing, and multi-modal understanding to keep Bland at the forefront of voice AI. What Makes You a Great Fit Deep ML expertise: 3+ years in machine learning with 1+ years focused on speech, or conversational AI. You've shipped ML systems that real users depend on. Experience with TTS/STT systems: You get your hands dirty with any new emerging technologies in this space, and are implementing novel solutions. Specialist: You don't have to have experience with the entire STT, LLM, TTS stack. We want someone who can narrow down on a specific problem and understand it in and out. Production experience: You've built and scaled ML infrastructure from 0-1 and 1-100. You know the difference between a research prototype and a system that works at enterprise scale. Full-stack mindset: Comfortable working across the entire ML pipeline-data, training, inference, monitoring, and everything in between. Startup DNA: You've thrived in fast-moving environments where you own outcomes, not just tasks. You're comfortable with ambiguity and excited by the challenge of figuring things out. Bonus Points If You Have Experience with real-time speech processing, TTS/ STT, or telephony systems Background in large-scale distributed training and inference Experience with conversational AI, chatbots, or voice assistants PhD in ML/AI or equivalent research experience How You Show Up Ownership mindset: You take full responsibility for your systems' performance and never wait for someone else to solve problems you can tackle. Quality obsessed: You care deeply about the craft-our agents should sound truly human, not like phone trees. Data-driven: You measure everything, run rigorous experiments, and let results guide decisions. Collaborative: You work seamlessly with engineers, deployment teams, and customers to deliver solutions that actually work. Relentless: You push through ambiguous challenges and complex technical problems until you find solutions. Benefits and Pay: Healthcare, dental, vision, all the good stuff Meaningful equity in a fast-growing company Every tool you need to succeed Beautiful office in Jackson Square, SF with rooftop views If you don't have the perfect experience that is fine! We're a bunch of drop-outs and hackers. Working at a start-up is really hard. We work a lot and we figure things out on the fly. Please note, however, for this position machine learning experience at an United States based company is required. Compensation Range: $140,000-$250,000 #J-18808-Ljbffr

At Protegrity, we lead innovation by using AI and quantum-resistant cryptography to transform data protection across cloud-native, hybrid, on-premises, and open source environments. We leverage advanced cryptographic methods such as tokenization, format-preserving encryption, and quantum-resilient techniques to protect sensitive data. As a global leader in data security, our mission is to ensure that data isn't just valuable but also usable, trusted, and safe. Protegrity offers the opportunity to work at the intersection of innovation and collaboration, with the ability to make a meaningful impact on the industry while working alongside some of the brightest minds. Together, we are redefining how the world safeguards data, enabling organizations to thrive in a GenAI era where data is the ultimate currency. If you're ready to shape the future of data security, Protegrity is the place for you. Protegrity is looking for a Machine Learning Engineer (PhD or MS Required) Location: Menlo Park, CA (In-office, Mon-Thu) The global data privacy software market is projected to grow from $2.36 billion in 2022 to $25.85 billion by 2029. Join us on this journey and make an impact with one of the top 25 global software providers. We look forward to making our world become a better place with you on our team. About the Role This role is designed for a PhD or MS graduate in Computer Science with 2+ years of GenAI experience or equivalent technical projects. You'll work on securing AI workflows and building agentic tools in a collaborative, fast-paced environment. Responsibilities Develop and test GenAI architectures using agentic coding IDEs. Conduct experiments and summarize findings. Present research and experimental results to the team. Fine-tune LLMs and embedding models. Apply ML algorithms to large datasets. Process structured and unstructured data. Participate in architectural design and roadmap discussions. Qualifications PhD or MS in Computer Science. 2+ years GenAI experience or equivalent projects. 3-5 years Python experience. Experience with PyTorch, TensorFlow. Solid understanding of ML algorithms and metrics. Exposure to data security practices. Strong collaboration and learning mindset. Why Choose Protegrity Become a member of a leading Data Protection, Privacy and Security company during one of the best market opportunities to come along in a generation. Competitive Compensation/Total Reward Packages that include: Health Benefits (Health/Dental/Vision) Paid Time Off (PTO) 401K Annual Bonus Incentives Short and Long Term Disability Work on global projects with diverse, energetic, team members who respect each other and celebrate differences Talent First Workforce Should you accept this position, you will be required to consent to and successfully complete a background investigation. This may include, subject to local laws, verification of extended education and additional criminal and civil checks. We offer a competitive salary and comprehensive benefits with generous vacation and holiday time off. All employees are also provided access to ongoing learning & development. Ensuring a diverse and inclusive workplace is our priority. We are committed to an environment of acceptance where you are free to bring your full self to work. All qualified applicants and current employees will not be discriminated against on the basis of race, color, religion, sex, sexual orientation, gender identity, age, national origin, disability or veteran status. Please reference Section 12: Supplemental Notice for Job Applicants in our Privacy Policy to inform you of the categories of personal information that we collect from individuals who inquire about and/or apply to work for Protegrity USA, Inc., or its parent company, subsidiaries or affiliates, and the purposes for which we use such personal information. #J-18808-Ljbffr

Software Engineering Mountain View, California Machine Learning Engineering TL, Behavior Planning Who we are Aurora's mission is to deliver the benefits of self-driving technology safely, quickly, and broadly. The Aurora Driver will create a new era in mobility and logistics, one that will bring a safer, more efficient, and more accessible future to everyone. At Aurora, you will tackle massively complex problems alongside other passionate, intelligent individuals, growing as an expert while expanding your knowledge. For the latest news from Aurora, visitaurora.tech or follow us on LinkedIn . Aurora hires talented people with diverse backgrounds who are ready to help build a transportation ecosystem that will make our roads safer, get crucial goods where they need to go, and make mobility more efficient and accessible for all. We are seeking a ML Engineering TL to join the Behavior Planning Team. This is a high-impact role where you will sit at the frontier of autonomous vehicle technology. You won't just be applying AI and ML methods; you will be advancing the state-of-the-art for how a self-driving system reasons about the world, interacts with humans on the road, and masters complex, high-dimensional decision-making. In this role, you will Define the architecture of our onboard planning models: Develop and deploy large-scale models trained with Imitation Learning and Reinforcement Learning that enable the Aurora Driver to navigate complex environments with human-like fluidity and superhuman safety. Build our next-generation simulation engine: Architect cutting-edge offboard foundation models that power our simulation engine, creating realistic "world models" to test the Aurora Driver against an infinite variety of edge cases. Revolutionize evaluation: Develop powerful offboard critic models that can evaluate driving behavior at scale, identifying subtle nuances in comfort, progress, and safety that traditional heuristics miss. Bridge research and production: Reach new frontiers of autonomous driving technology by pushing forward the state-of-the-art, but also deploy your models on real production vehicles that drive on public roads and must meet the highest standards of safety. Mentor and lead: Serve as a technical lead, guiding junior engineers and shaping the long-term roadmap for ML-based planning at Aurora, including the onboard and off-board ecosystem that is needed to support it. Required Qualifications MS or PhD in Robotics, Machine Learning, Computer Science, or a related quantitative field, or equivalent practical experience. 8 + years of experience developing state-of-the-art ML models, either in a research or production setting. Hands-on experience working on Imitation Learning or Reinforcement Learning applied to physical or simulated agents. Experience training large models on massive datasets using distributed computing. Fluency in Python, with a focus on writing high-performance, maintainable code. Deep experience with PyTorch (preferred) or another modern ML framework, and a mastery of modern ML architectures including Transformers and Diffusion Models. Desirable Qualifications A track record of publications in top-tier ML conferences (NeurIPS, ICML, CoRL, CVPR, AAAI). Experience deploying complex ML systems in production environments. Experience in developing generative models or neural simulators for synthetic data generation. Experience leading small or large teams to execute highly technical projects. The base salary range for this position is $126K - $181.5K per year. Aurora's pay ranges are determined by role, level, and location. Within the range, the successful candidate's starting base pay will be determined based on factors including job-related skills, experience, qualifications, relevant education or training, and market conditions. These ranges may be modified in the future. The successful candidate will also be eligible for an annual bonus, equity compensation, and benefits. #LI-JL261 #Mid-Senior Commitment to inclusion Aurora considers candidates without regard to their race, color, religion, national origin, age, sex, gender, gender identity, gender expression, sexual orientation, marital status, pregnancy status, parent or caregiver status, ancestry, political affiliation, veteran and/or military status, physical or mental disability, or any other status protected by federal or state law. Aurora considers qualified applicants with criminal histories, consistent with applicable federal, state, and local law. We are also committed to providing reasonable accommodations for qualified individuals with disabilities and disabled veterans in our job application procedures. If you need assistance or an accommodation due to a disability, you may contact us at careersiteaccommodations@aurora.tech . For California applicants, information collected and processed as part of your application and any job applications you choose to submit is subject to Aurora's California Employment Privacy Policy. #J-18808-Ljbffr

This is an opportunity with an early stage startup. (M-F, in Mountain View, CA) About the Role We're looking for an ML research-focused software engineer to join us on our mission to build AI superpowers for developers. What you'll do Train and fine-tune large language models Navigate high levels of uncertainty and prioritize high-value ML experiments to maximize product impact Demonstrate initiative and the ability to start and make progress on projects independently Swiftly design, track, and analyze experiment results. Meticulously document findings, conduct ablation studies, and synthesize data into actionable insights. Participate in the ML reading group and level up the team's knowledge of LLM training and infrastructure About you Strong software engineering skills. There are no pure research scientists at the company. Strong grasp of the feasibility frontier of CS, AI, and LLMs, from H100 bandwidth to GPT-4 capabilities to vector database performance. Deep curiosity about the code generation problem. Willingness to constantly re-examine priors in the face of new discoveries. Skilled in transforming successful experimental outcomes into robust, scalable features for the core product offering Experience training and iterating on large production neural networks in any domain (self-driving, language models, etc.) is a strong plus Familiarity with AI-powered developer tools like Copilot, ChatGPT, and others is a strong plus What we believe Our best work is done in person. The team goes in 5 days a week into our office in downtown Mountain View, CA (within walking distance of the Caltrain station). Research is in service of a better product. While we read many papers, we won't have time to write them. The best AI researchers have excellent software engineering skills and know that infrastructure and evaluation work are critical. #J-18808-Ljbffr

Software Engineering Mountain View, CA (HQ) About X, the Moonshot Factory X is a diverse group of inventors and entrepreneurs who build and launch technologies that aim to improve the lives of millions, even billions, of people. Our goal: 10x impact on the world's most intractable problems, not just 10% improvement. We approach projects that have the aspiration and riskiness of research with the speed and ambition of a startup. About the team We're X's AI for chemistry moonshot, applying AI to supercharge processes related to the manufacturing of existing chemical compounds. We're an agile team of experienced AI researchers, software engineers, and entrepreneurs working with partners and customers around the world. We're looking for people who are passionate about working at the intersection of AI and science. You'll be part of a fast‑paced team that has the agility and impact of an early‑stage company, while building on world‑class Google AI technology. About the role As a Machine Learning Engineer, you will be responsible for designing, developing, and deploying state‑of‑the‑art multi‑modal models. This role requires a deep passion for problem‑solving and experimentation, moving across the full range of applied machine learning tasks from initial prototyping to building full‑scale, robust solutions. While you will primarily focus on core product development, you will also serve as a critical bridge between cutting‑edge AI research and real‑world enterprise applications. You will be a key player in our mission to accelerate scientific discovery through machine learning. How you will have 10X impact Design and build end‑to‑end machine learning pipelines for training, evaluation, and deployment. Develop and fine‑tune sophisticated multi‑modal models using frameworks like PyTorch and libraries from the Hugging Face ecosystem. Leverage Google Cloud Platform (GCP) services to deploy and scale our ML models and infrastructure. Collaborate with cross‑functional teams of scientists and engineers to translate research ideas into production‑ready solutions. Stay current with the latest advancements in machine learning, particularly in multi‑modal learning and generative models. What you should have Bachelor's or Master's degree in Computer Science, Engineering, or a related technical field. At least 5 years of experience designing, building, and deploying ML solutions. Proven experience building and training deep learning models using PyTorch and the Hugging Face library. Demonstrated experience with Large Language Models (LLMs), multi‑modal models (e.g., Vision Language Models) and computer vision models (e.g., OCR). Strong proficiency with Google Cloud Platform (GCP) and its core services for machine learning and data processing. Excellent programming skills in Python and a solid understanding of software engineering best practices. Hands‑on experience with MLOps principles and tools (e.g., Kubeflow, MLflow, Vertex AI Pipelines) and DevOps practices (e.g., CI/CD, Docker, Kubernetes, Terraform). Experience with large‑scale data processing and database management (e.g., PostgreSQL, SQLAlchemy). Experience in start‑up or small team environments. It'd be great if you also had these A foundational knowledge of organic chemistry concepts or experience working with chemical data (e.g., SMILES strings, molecular graphs). Published research papers in relevant fields. Compensation The US base salary range for this full‑time position is $165,000 - $258,000 + bonus + equity + benefits. Within the range, individual pay is determined by work location and additional factors, including job‑related skills, experience, and relevant education or training. Your recruiter can share more about the specific salary range for your location during the hiring process. Please note that the compensation details listed in US role postings reflect the base salary only, and do not include bonus, equity, or benefits. Location and Relocation This position is located in Mountain View, CA. Are you able to commute to the office and/or are you willing to relocate? An Equal Opportunity Workplace At X, we don't just accept difference - we celebrate it, we support it, and we thrive on it for the benefit of our employees, our products and our community. We are proud to be an equal opportunity workplace and an affirmative action employer. We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or veteran status. We also consider qualified applicants without regard to criminal histories, consistent with applicable legal requirements. #J-18808-Ljbffr

Join our AI platform team as a Machine Learning Engineer, focusing on Generative AI and large language models (LLMs) to enhance Corvic's deep insight capabilities. Responsibilities Develop and deploy generative AI and LLMs within our product ecosystem Collaborate with cross-functional teams to integrate AI technologies seamlessly Manage AI models from development to deployment, focusing on natural language processing Qualifications Bachelor's degree in Computer Science, Software Engineering, or a related field Expertise in Python, TensorFlow, or PyTorch Experience with cloud platforms (AWS, GCP, Azure) and container technologies (Docker, Kubernetes) Knowledge of machine learning techniques such as embeddings, deep models, and feature engineering Strong background in generative AI, especially transformers and NLU models, and how to manage them Comfort in fast-paced and evolving work environments #J-18808-Ljbffr

About Voltai Voltai is developing world models, and agents to learn, evaluate, plan, experiment, and interact with the physical world. We are starting out with understanding and building hardware; electronics systems and semiconductors where AI can design and create beyond human cognitive limits. About the Team Backed by Silicon Valley's top investors, Stanford University, and CEOs/Presidents of Google, AMD, Broadcom, Marvell, etc. We are a team of previous Stanford professors, SAIL researchers, Olympiad medalists (IPhO, IOI, etc.), CTOs of Synopsys & GlobalFoundries, Head of Sales & CRO of Cadence, former US Secretary of Defense, National Security Advisor, and Senior Foreign-Policy Advisor to four US presidents. What we're Looking For Strong AI/ML engineering skills from top tier CS, EECS, Math and Physics programs. Proven track record of delivering AI/ML projects from concept to production. Hands‑on experience fine‑tuning and deploying large language models (LLMs) in production environments. Prior experience working with multi‑modal models (e.g., combining text, image, or audio inputs). Bonus Points Background in competitive programming. Contributions to open‑source initiatives. Notable awards or publications in leading journals/conferences. Experience thriving in a fast‑paced, hyper‑growth startup environment. #J-18808-Ljbffr

Gauss Labs is looking for a passionate and talented AI Engineer for developing cutting-edge Industrial AI solutions that will normalize the standard of AI for manufacturing. We are working with the world's best manufacturing customers while accessing the vast amount of real data from their manufacturing processes. We apply state-of-the-art AI technologies to the data and develop unprecedented AI/ML solutions to transform manufacturing to the next level. As an AI Engineer, you will be responsible for translating cutting-edge AI and machine learning research into robust, scalable software solutions. Your work will ensure the seamless integration of models into production environments and contribute to the broader success of AI initiatives across the company. You will develop AI software by working with seasoned Applied Scientists, Software Engineers, and Program Managers located in Palo Alto, California, USA and Seoul, South Korea. Responsibilities Collaborate with AI Scientists to understand model requirements and design scalable, efficient ML pipelines. Build and maintain reliable, performant infrastructure for data processing, model training, evaluation, and deployment. Own the end-to-end implementation of ML systems from research prototypes to production-grade code. Optimize model training/inference, latency, and resource usage to meet performance and system constraints. Develop monitoring, observability, and CI/CD tooling to support the full ML lifecycle in staging and production environments. Ensure engineering best practices in code quality, testing, documentation, and software reliability. Interface with product and engineering teams to understand requirements and drive integration of AI systems into user-facing applications. Key Qualifications BS in Computer Science, Electrical Engineering, Machine Learning, or related technical field. Proficiency in one or more modern programming languages such as Python, C++, or Java with an understanding of algorithms and data structures. Strong expertise in Python data science stack (NumPy, Pandas) and ML/DL frameworks (scikit-learn, PyTorch, TensorFlow) for end-to-end model development. 3+ years building production-ready ML infrastructure, including data pipelines, training/inference workflows, and deployment automation. Solid understanding of software engineering best practices: version control (Git), unit testing, code review, and CI/CD. Familiarity with containerization and orchestration tools (e.g., Docker, Kubernetes). Experience developing software applications and services with an understanding of design for scalability, performance, and reliability. Strong problem-solving skills, attention to detail, and a collaborative mindset when working with research and product teams. Preferred Qualifications MS or Ph.D. in Computer Science, Electrical Engineering, Machine Learning or related technical field. Knowledge of professional software engineering practices including source control management, code reviews, testing, and continuous integration/deployment. Experience in optimizing training and inferencing structures for large scale ML/DL models. Experience deploying machine learning models into production environments (e.g., batch, real-time, or edge deployments). Experience in distributed/parallel systems, information retrieval, networking, and systems software development. Development experience in a cloud service environment such as Amazon AWS, MS Azure, or Google Cloud Platform. #J-18808-Ljbffr

Member of Technical Staff, Machine Learning Engineer What You'll Work On At Reinforce Labs, we partner directly with customers to build AI systems that enhance the safety and reliability of their complex, high-impact applications. In this role, you'll engage directly with clients to deeply understand their specific challenges, conduct thorough data analysis, and deliver trust and safety AI solutions. You'll implement advanced machine learning techniques including LLMs, reinforcement learning, and deep learning - often translating cutting‑edge research papers into practical applications. This position combines technical depth with significant client‑facing responsibilities. You'll tackle high‑stakes problems with unclear solutions but real‑world consequences. Key responsibilities include: Conducting technical research with customers to map critical workflows and developing sophisticated AI probes that identify potential edge‑case failures Performing comprehensive data analysis to build specialized classifiers that detect subtle risks, violations, and anomalies Implementing and fine‑tuning generative models that can simulate rare or high‑risk scenarios Designing systems that enhance human decision‑making under pressure Our success is measured by real‑world impact. You'll develop and ship solutions that protect people and platforms at scale while building trusted relationships with the clients who depend on our technology. Must‑Have Experience with ML and deep learning frameworks such as PyTorch or TensorFlow Ability to translate cutting‑edge research papers into practical AI applications Hands‑on experience training and deploying machine learning models in production Proficiency in Python Nice‑to‑Have Experience partnering with trust & safety leads and background in trust & safety, moderation, AI safety, or security‑focused ML to deliver state‑of‑the‑art solutions [a big plus] An advanced degree in computer science or a related field LLM and agentic framework experience Experience in early‑stage startups Experience in customer‑facing AI technical solutions Contributions to high value AI/ML community projects Who We Are Reinforce Labs is a small team with a clear mission: to use AI to make critical systems safer, not just smarter. We value: Willingness to learn new things, take on diverse tasks, and challenge yourself Hands‑on technical excellence Ownership Ability to handle ambiguity Fast, results‑driven delivery #J-18808-Ljbffr

Assembled builds the infrastructure that underpins exceptional customer support, empowering companies like CashApp, Etsy, and Robinhood to deliver faster, better service at scale. With solutions for workforce management, BPO collaboration, and AI-powered issue resolution, Assembled simplifies the complexities of modern support operations by uniting in-house, outsourced, and AI-powered agents in a single operating system. Backed by $70M in funding from NEA, Emergence Capital, and Stripe, and driven by a team of experts passionate about problem-solving, we're at the forefront of support operations technology. What we build on Forecasting & Scheduling Contact-volume forecasting: data pipelines, statistical/ML models and inference services that predict ticket volumes, agent demand and time to resolution. Queueing simulation: realistic models of synchronous (phone, chat) and asynchronous (email, messaging) queues that forecast wait times, staffing demand considering clearing weekend backlogs while still receiving new tickets. Scheduling tooling: a calendar‑like UI that lets managers create and adjust rosters for thousands of agents while respecting preferences, labor laws and SLAs. Agent empowerment: self‑service pages for shift swaps, time‑off requests and overtime management. What you'll do with us Lead the architecture and delivery of new ML features end-to-end: research → prototype → production. Drive technical roadmaps, code reviews and design sessions to share your knowledge with the rest of the team. Mentor engineers, unblock thorny problems and act as subject‑matter expert for data science topics. Collaborate with Product and Design to turn unclear customer problems into shippable solutions. What we're after 5+ years shipping production time‑series forecasts or similar ML systems. Proficient in a typed backend language (Go, Java or Rust) and comfortable with Python for research. Experience owning services in AWS or similar cloud. Demonstrated technical leadership: design docs, trade‑off decisions, mentoring, incident ownership. Product mindset: ability to balance model accuracy, latency, cost and user experience. Even‑better‑ifs Prior work on large‑scale scheduling or optimization problems (e.g. nurse‑rostering). Exposure to Kubernetes, Terraform or CDK. Front‑end empathy; willing to tweak a React component when needed. Our U.S. benefits Generous medical, dental, and vision plans. Paid company holidays, sick time, and unlimited time off. Monthly credits to spend on professional development, general wellness, Assembled customers, and commuting. Paid parental leave. Hybrid work model with catered lunches everyday (M-F), snacks, and beverages in our SF & NY offices. 401(k) plan enrollment. #J-18808-Ljbffr