Machining engineer jobs in Hayward, CA - 6,294 jobs

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

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

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

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

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

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

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

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

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

Aquabyte is seeking a Machine Learning Engineer to develop and deploy algorithms for fish farms worldwide. You'll be responsible for software and machine learning model development of our on‑camera and cloud software. Our Mission Aquabyte is on a mission to revolutionize the sustainability and efficiency of aquaculture. By making fish farming more efficient and viable, we aim to promote healthy production of low‑carbon protein and mitigate one of the biggest causes of climate change. Aquaculture is the single fastest growing food‑production sector in the world, and now is the time to define how technology is used to harvest the sea and preserve it for generations to come. Our Product We focus on helping salmon farmers better understand their fish population and make environmentally sound decisions. Through custom underwater cameras, computer vision, and machine learning we quantify fish weights, detect health status, and generate optimal feeding plans in real time. Our product operates at three levels: on‑site hardware for image capture, cloud pipelines for data processing, and a user‑facing web application. There are hundreds of moving pieces and many fascinating challenges across all levels of the stack. The Role As a Machine Learning Engineer you will be responsible for developing machine learning models and pipelines, interacting with databases and data infrastructure, conducting in‑depth data analytics, and building statistical data inference models of biological processes. This AI team develops image and video inference pipelines to estimate the weight, health and behavior of individual fish and fish populations. You will work closely alongside experienced engineers. Required Qualifications BS/MS in a relevant technical degree 3+ years of experience with data science Strong coding ability; strong grasp of Python, SQL Strong data analytics, modeling & machine learning skills Strong data pipeline and data management skills Strong software engineering skills; knowledge of best practices, testing, and deployment Bonus Qualifications Familiarity with snowflake, dbt, airflow, pandas Experience with Docker and cloud software development (i.e. AWS) Benefits Competitive salaries and generous equity Unlimited vacation policy Flexible working hours Medical, vision, & dental insurance Retirement matching plan Potential travel to Norway Evolve in a fast‑paced environment Be able to shape a business in its early days Get ideas, feedback, and suggestions from colleagues Mentorship opportunities; we dedicate resources to support your growth Compensation $130,000 - $170,000 a year. Aquabyte takes a market‑based approach to salary and equity. Pay varies based on qualification, experience, interview performance, and location. Aquabyte is a private company headquartered in San Francisco, supported by NEA, Costanoa Ventures, and other investors. We strongly encourage applicants even if you don't satisfy all requirements, and we will get back to you as soon as possible. #J-18808-Ljbffr

About Treering Treering, a Silicon Valley-based tech company, helps people preserve and celebrate their memories. By combining just-in-time digital printing with the power of AI tools, Treering delivers personalized keepsakes that celebrate important milestones and events. About the Role We are seeking a highly skilled AI Engineer to lead the maintenance and enhancement of our existing AI solutions. This role will be crucial in ensuring the smooth operation and continuous improvement of our AI-driven product generation process. You will be working with a suite of APIs designed to analyze, score, and rank photos, making critical decisions to automatically generate photobooks, yearbooks, and other print-on-demand products. Responsibilities AI Systems Maintenance: Maintain and optimize existing AI solutions, including APIs for photo analysis, scoring, and ranking. Enhancement and Development: Develop and enhance AI models and algorithms to improve the accuracy and efficiency of automated product generation. API Management: Oversee and manage the suite of APIs used in our AI solutions. Cloud Infrastructure: Utilize and manage AWS services such as Rekognition, Lambda, SageMaker, Step Functions, SQS, OpenSearch, S3, and RDS. State Machine Management: Design, implement, and manage state machines for orchestrating AI processes. Programming: Write and maintain Python code for AI models and API development. Monitoring and Troubleshooting: Monitor AI system performance, troubleshoot issues, and implement solutions. Collaboration: Work closely with cross-functional teams to integrate AI solutions into the overall product workflow. What We're Looking For Bachelor's or higher degree in Computer Science, Data Science, Artificial Intelligence, or a related field. 6+ years of experience in AI development and deployment. Strong proficiency in Python programming. Extensive experience with AWS services, including Rekognition, Lambda, SageMaker, Step Functions, State Machines, SQS, OpenSearch, S3, and RDS. Experience with API development and management. Solid understanding of machine learning algorithms and techniques. Excellent problem-solving and analytical skills. Strong communication and collaboration skills. Strong leadership and mentoring abilities. Why Join Treering? Impact: Your work directly contributes to modernizing the yearbook industry. Innovation: Work with cutting-edge technologies in an agile environment. Growth: Opportunities for career advancement and professional development. Culture: A collaborative, inclusive, and supportive team environment. Benefits Comprehensive medical, dental, vision, life/AD&D, and disability coverage Pre-tax savings/spending plans, including HSA employer contributions Parental Leave Benefits Pre-tax and Roth 401(k) plan with an employer contribution Flexible vacation for salaried Twelve paid holidays throughout the year $180,000 - $200,000 a year If you are an experienced software engineer who thrives in a dynamic environment and is passionate about designing and implementing innovative web solutions, we invite you to apply. #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

A leading technology research organization in California seeks a machine learning engineer to develop foundational models for robotics. The role involves enhancing hardware infrastructure, building APIs for data handling, and designing evaluation metrics. Ideal candidates have hardware and communication protocol experience, along with strong software skills in Python and a passion for robotics. Competitive pay ranges from $176,000 to $264,000, with a comprehensive benefits package. #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 user experiences that best fit their goals, while also optimizing for business metrics that support long-term sustainability. We power recommendation and search systems across multiple surfaces, such as the home feed, search results, and adaptive study modes, as well as decision systems in ads and notifications that determine the timing and nature of key interventions. Within this organization, this role is responsible for the predictive and decisioning models that drive monetization, retention, activation and goal‑aligned study guidance. These systems balance immediate impact with long‑term user value and must integrate seamlessly into Quizlet's product architecture. As a Staff Machine Learning Engineer on the Personalization & Recommendations team, you will lead both the modeling efforts and the technical integration work required to bring complex ML systems into production. This includes designing predictive and prescriptive models (such as conversion propensity, churn risk, LTV, sequential decisioning, and timing optimization) and collaborating closely with product and infrastructure engineering to ensure these models can be safely and cleanly embedded into existing product workflows. A major part of this role involves identifying dependencies within the product codebase, defining integration contracts with cross‑functional partners, and shaping technical solutions that allow ML‑driven decisioning to operate reliably, efficiently, and maintainably at scale. You'll work closely with product managers, data scientists, platform engineers, backend engineers, and fellow ML engineers to deliver ML‑driven experiences that drive engagement, satisfaction, and measurable business outcomes. About the Role As a Staff Machine Learning Engineer on the Personalization & Recommendations team, you will lead the development of ML systems that decide what action Quizlet should take for a learner, when that action should occur, and under what constraints. This role focuses on action selection and policy design rather than content ranking alone, and requires deep ownership of both modeling and production integration. You will own the full lifecycle of these systems (from problem framing and model development to integration, deployment, and long‑term reliability), working closely with product, infrastructure, and backend engineering partners. A core responsibility of this role is embedding model‑driven decisions into Quizlet's product in a way that is safe, observable, and maintainable, including identifying dependencies, defining clean interfaces, and ensuring robust fallback behavior. Your work will directly influence monetization, retention, activation and goal‑aligned study guidance, requiring you to balance short‑term business impact with long‑term learner value and product integrity. 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 facilitates increased work efficiency, team partnership, and supports growth as an employee and organization. In this role you will Lead the design and development of predictive and prescriptive models (e.g., conversion propensity, churn risk, LTV, uplift, sequential decisioning, and timing optimization) that drive learner‑facing decisions across monetization, lifecycle, and study guidance surfaces. Design and build decisioning and policy models that determine learner‑facing actions across product surfaces, including monetization, lifecycle, and study guidance use cases. These systems operate under real‑world product constraints and must optimize across multiple, sometimes competing objectives You will work on problems such as: determining when and how to present paywalls, discounts, or value exchanges, selecting personalized study modes or interventions based on learner state and intent, triggering retention or churn‑prevention actions at the right moment, and balancing immediate conversion or revenue with long‑term engagement and learning outcomes This role emphasizes: multi‑objective optimization across monetization, retention, and user experience, timing‑and‑eligibility‑aware decisioning rather than static predictions, and consistent action selection across sessions and surfaces Evaluation approaches that connect offline modeling metrics to online experimental outcomes Apply advanced techniques such as uplift modeling, survival analysis, sequential decisioning, and other policy‑based approaches, bringing them into production in collaboration with cross‑functional partners Lead the end‑to‑end productionization of ML systems, from modeling through integration, ensuring models can be safely, cleanly, and reliably embedded into existing product workflows Identify upstream and downstream dependencies within the product codebase and data ecosystem, and proactively address integration risks Define and negotiate clean integration boundaries, including API contracts, data interfaces, decision schemas, and fallback strategies, in collaboration with product and infrastructure engineering Partner closely with Infrastructure Engineering to design scalable, resilient, and observable model‑serving paths that integrate with Quizlet's application stack Embed model‑driven decisioning logic into backend and product flows in ways that are maintainable, testable, and compatible with existing systems Build and maintain end‑to‑end pipelines for feature engineering, training, evaluation, deployment, and monitoring, ensuring training-serving consistency Improve latency, throughput, reliability, and observability of real‑time and near-real‑time inference systems operating at scale. Translate product goals (conversion, retention, revenue, engagement) into clear modeling objectives and technical specification. Collaborate closely with product managers, backend engineers, and infrastructure partners to ensure ML systems fit naturally into the existing architecture without introducing brittle dependencie Develop evaluation frameworks that tie offline metrics to online A/B results, ensuring changes are measurable, interpretable, and aligned with product impact Clearly communicate assumptions, trade‑offs, risks, and technical constraints to both technical and non‑technical stakeholders Provide technical leadership for ML‑driven decision systems, guiding the organization toward unified policy models and consistent action‑selection frameworks across surfaces Mentor engineers and scientists, setting a high bar for modeling rigor, production quality, experimentation discipline, and responsible ML Shape long‑term strategy for scalable, maintainable ML decisioning, bringing modern approaches-including sequential decisioning and RL‑adjacent techniques-into production where appropriate What you bring to the table 8+ years of applied ML or ML‑heavy engineering experience, with a track record of shipping production models that drive measurable business impact Deep expertise in classical ML techniques (e.g., boosted trees, GLMs, survival models, uplift modeling) Experience with reinforcement learning, contextual bandits, or sequential decision‑making Strong engineering skills with Python and common ML frameworks (scikit‑learn, PyTorch, XGBoost, LightGBM, etc.) Demonstrated experience integrating ML systems into complex product architectures, ideally including monolithic applications Experience defining integration boundaries, solving backend/ML interface issues, and collaborating with infra teams on serving patterns Strong understanding of experimentation design, causal analysis, and the relationship between offline and online evaluation Excellent communication skills for conveying technical constraints and integration trade‑offs A strong ownership mindset centered on reliability, maintainability, and long‑term system health Bonus points if you have Background in causal ML or uplift modeling Experience with paywall optimization, monetization systems, or churn modeling Knowledge of real‑time inference architectures, feature stores, or streaming systems Publications or open‑source contributions in ML, RL, causal inference, or system integration 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 $190,000 - $274,500, 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. #LI-FT #J-18808-Ljbffr

Our mission is to create advanced robots that can operate in complex environments, reducing human risk in conflict zones and enhancing efficiency in labor-intensive industries. We are on the lookout for extraordinary engineers and scientists to join our team. Your previous experience in robotics isn't a prerequisite - it's your talent and determination that truly count. We expect that many of our team members will bring diverse perspectives from various industries and fields. We are looking for individuals with a proven record of exceptional ability and a history of creating things that work. Our Culture We like to be frank and honest about who we are, so that people can decide for themselves if this is a culture they resonate with. Please read more about our culture here ****************************** . Who should join: You like working in person with a team in San Francisco. You deeply believe that this is the most important mission for humanity and needs to happen yesterday. You are highly technical - regardless of the role you are in. We are building technology; you need to understand technology well. You care about aesthetics and design inside out. If it's not the best product ever, it bothers you, and you need to “fix” it. You don't need someone to motivate you; you get things done. Why are We Hiring for this Role: Design, develop, and optimize reinforcement learning algorithms for real-time control and locomotion of humanoid robots. Integrate learned policies into real-world robot platforms with hardware-in-the-loop validation. Collaborate with mechanical, perception, and embedded systems teams to ensure tight integration between hardware and software. Apply advanced techniques such as curriculum learning, domain randomization, and sim2real transfer to improve policy generalization. Analyze and optimize control performance with a focus on robustness, energy efficiency, and adaptability. Contribute to the continuous development of our in-house RL training pipelines and tooling. 2+ years of experience in reinforcement learning applied to robotics or control systems. Strong understanding of classical and modern control theory, locomotion dynamics, and optimization techniques. Hands-on experience with physics simulation environments (e.g., MuJoCo, Isaac Gym, PyBullet). Proficiency in Python and/or C++ for algorithm development and deployment. Experience with deep learning frameworks (e.g., PyTorch, TensorFlow). Familiarity with ROS/ROS2 and real-time robotic systems. What Kind of Person We Are Looking For: 2+ years of experience in machine learning (NNs, LVMs) and reinforcement learning applied to robotics or similar realtime environments. Hands-on experience with physics simulation environments (e.g., MuJoCo, Isaac Lab). Proficiency in Python and C++ for algorithm development and deployment. Experience with deep learning frameworks (e.g., PyTorch, JAX, TensorFlow). Familiarity with ROS/ROS2 and real-time robotic systems. Experience deploying RL algorithms on physical robots. Experience with high-performance computing for distributed training. Contributions to open-source RL or robotics projects. M.Sc. or Ph.D. in Robotics, Computer Science, Mechanical Engineering, or a related field. We provide market standard benefits (health, vision, dental, 401k, etc.). Join us for the culture and the mission, not for the benefits. The annual compensation is expected to be between $80,000 - $1,000,000. Exact compensation may vary based on skills, experience, and location. #J-18808-Ljbffr

Gritt Robotics is building physical AI systems that automate the construction of renewable energy infrastructure - dramatically accelerating the global transition to clean energy. Founded by experts from Carnegie Mellon, MIT, and Stanford, and backed by top-tier VCs (early investors in Uber, Notion, and Lyft), Gritt is at the forefront of climate-impact robotics innovation. We are looking for a Lead Robotics AI Engineer to join our early team and play a foundational role in developing intelligent, autonomous robots that operate in complex, unstructured outdoor environments. This role is ideal for someone passionate about robotics, eager to work hands‑on, and ready to lead technical development in a high‑impact, mission‑driven startup. What You'll Do Design, develop, and deploy state‑of‑the‑art AI systems for robotic manipulation and navigation Drive AI architecture decisions in perception, planning, and control Build and optimize simulation‑driven training and validation pipelines Work across sensor modalities including vision, LIDAR, and tactile sensing Improve model performance in challenging outdoor conditions and on real hardware Optimize training and inference for GPU environments Collaborate with cross‑functional engineering teams and grow into leadership roles What We're Looking For M.S. or Ph.D. in Robotics, Computer Vision, Machine Learning, or a related field - or equivalent experience 4+ years of hands‑on experience developing and deploying AI solutions in robotics (manipulation, navigation, or similar) Deep expertise in computer vision, machine learning, deep learning, or reinforcement learning Familiarity with techniques like VLMs, deep RL, and imitation learning Proficient in Python and modern frameworks such as PyTorch Comfortable working autonomously and taking ownership in fast‑paced environments Strong analytical, debugging, and problem‑solving skills Must be legally authorized to work in the U.S. Why Gritt Tackle some of the hardest problems in robotics with climate‑impacting outcomes Join a deeply technical founding team and grow into key leadership roles Competitive salary, equity, and full health/vision/dental coverage Build the future of physical AI for a sustainable planet #J-18808-Ljbffr

A robotics technology company based in San Francisco is seeking a Machine Learning Engineer to lead the development of autonomous capabilities using Vision-Language-Action models. This role involves designing data collection pipelines, building data processing systems, and deploying models to edge devices. The ideal candidate has over 3 years of hands-on experience in ML, particularly in robotics or computer vision. Join us to shape the future of automated labor and transform industries with innovative robotic solutions. #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

A pioneering robotics company in San Francisco is seeking a Forward Deployed Robotics Engineer. This hands-on role involves owning customer projects from start to finish, collaborating closely with scientists to create innovative robotic solutions. Candidates should have 1-3 years of experience in robotics systems, strong problem-solving skills, and a degree in a related field. The position is 100% onsite, emphasizing the need for a dedicated team player ready to contribute to groundbreaking research and development. #J-18808-Ljbffr