Senior ML Engineer Jobs
Senior ML Engineer jobs are open across tech, finance, healthcare, and defense, from mid-level to staff and principal, with specializations in NLP, computer vision, and recommendation systems. Find a role that fits from the openings below and apply directly.
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INTRODUCTION
Chef Robotics is accelerating the deployment of intelligent machines in the physical world, starting with food production — the sector facing the largest labor shortage in the U.S., with 1.14M unfilled jobs today and 3.1M projected by 2030. These roles can't be offshored, making robotics essential to keeping production onshore and strengthening America's manufacturing base. Our AI-powered robots automate food prep and assembly in commercial kitchens and food manufacturing, and have already produced over 110 million meals in production — generating the world's largest proprietary dataset for deformable food manipulation. Backed by investors including Kleiner Perkins, Construct, Bloomberg Beta, and Promus Ventures, and built by a team from Cruise, Zoox, Google, Tesla, and Amazon Robotics, Chef is rapidly scaling with multiple multi-year contracts and a mission to put an intelligent robot in every commercial kitchen.
ABOUT THE ROLE
The next leap in food robotics won't come from hand-tuned policies for individual ingredients — it will come from foundation models that generalize across thousands of food types, kitchen configurations, and manipulation scenarios out of the box. At Chef, we're building that model: the Food Foundation Model. As a Senior ML Engineer, Foundation Models, you will work at the frontier of large-scale robot learning: training and fine-tuning the Food Foundation Model, building the data infrastructure that feeds it, and deploying it onto physical robots in production kitchens. You'll bridge research and engineering — translating advances from the latest policy learning, generative modeling, and world model literature into systems that handle real food, with real end effectors, at real throughput. Your models won't just benchmark well; they'll serve millions of meals. We are a small, high-ownership team. We work onsite five days a week and move with startup urgency.
YOUR ROLE AND RESPONSIBILITIES
- Define the architecture, training objectives, and learning approach for the Food Foundation Model — evaluating tradeoffs across generalization, sample efficiency, and deployment constraints
- Investigate and evaluate the latest foundation model architectures — including VLAs, world models, JEPA-style joint embedding models, diffusion policies, and emerging approaches — and assess their applicability to Chef's manipulation and generalization challenges
- Design pre-training, fine-tuning, and alignment pipelines that improve the model's ability to generalize across new food types, kitchen configurations, and end effector types with minimal retraining
- Develop evaluation frameworks that measure real-world generalization and long-horizon reliability — not just offline benchmark accuracy
- Collaborate with the data and platform teams on training data requirements, augmentation strategies, and model serving constraints
- Stay current with the research frontier — reading and critically evaluating recent work from CoRL, RSS, NeurIPS, ICML, and ICLR and forming clear views on what's relevant to production manipulation
BASIC QUALIFICATIONS
- MS or PhD in Machine Learning, Robotics, Computer Science, or a related field — or equivalent industry experience
- 5+ years of experience implementing and deploying ML models for real-world robotics applications
- Hands-on experience with large-scale model training: pre-training, fine-tuning, and post-training alignment pipelines
- Familiarity with modern policy and generative model architectures — diffusion models, transformers, behavior cloning, or large-scale multimodal models
- Strong PyTorch skills and experience building reliable, production-quality training and evaluation infrastructure
- Solid software engineering fundamentals in Python; able to write maintainable code across research and production codebases
- Track record of taking models from research prototype to deployed system on physical hardware
PREFERRED QUALIFICATIONS
- Experience with world models or generative models for robot planning and prediction
- Background in large-scale distributed training (multi-node GPU clusters, FSDP, DeepSpeed)
- Familiarity with simulation environments (MuJoCo, Isaac Sim, Genesis) for synthetic data generation and domain randomization
- Experience deploying models to edge hardware (ONNX, TensorRT, quantization, performance profiling)
- Prior work with contact-rich manipulation, deformable object handling, or food robotics
- Publications at top venues: CoRL, RSS, ICRA, NeurIPS, ICML, ICLR
Chef Robotics is solving one of the hardest problems in AI and robotics — and we ship. Our robots are in production today, generating real data that trains the next generation of food AI. Backed by Kleiner Perkins, Construct, Bloomberg Beta, and Promus Ventures, and built by a team from Cruise, Zoox, Google, Tesla, and Amazon Robotics, we're scaling fast with multiple multi-year enterprise contracts. If you want to build physical AI with real-world deployments and real impact, Chef is the place.
We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.
Chef is an early-stage startup where equity is a major part of the compensation package. Our salary ranges are determined by role, level, and location. The range displayed on each job posting reflects the minimum and maximum target for new hire salaries for the position. Within the range, individual pay is determined by additional factors, including job-related skills, experience, and relevant education or training. In addition to salary and early-stage equity, we offer a comprehensive benefits package that includes medical, dental, and vision insurance, commuter benefits, flexible paid time off (PTO), catered lunch, and 401(k) matching.
INTRODUCTION
Chef Robotics is accelerating the deployment of intelligent machines in the physical world, starting with food production — the sector facing the largest labor shortage in the U.S., with 1.14M unfilled jobs today and 3.1M projected by 2030. These roles can't be offshored, making robotics essential to keeping production onshore and strengthening America's manufacturing base. Our AI-powered robots automate food prep and assembly in commercial kitchens and food manufacturing, and have already produced over 110 million meals in production — generating the world's largest proprietary dataset for deformable food manipulation. Backed by investors including Kleiner Perkins, Construct, Bloomberg Beta, and Promus Ventures, and built by a team from Cruise, Zoox, Google, Tesla, and Amazon Robotics, Chef is rapidly scaling with multiple multi-year contracts and a mission to put an intelligent robot in every commercial kitchen.
ABOUT THE ROLE
The next leap in food robotics won't come from hand-tuned policies for individual ingredients — it will come from foundation models that generalize across thousands of food types, kitchen configurations, and manipulation scenarios out of the box. At Chef, we're building that model: the Food Foundation Model. As a Senior ML Engineer, Foundation Models, you will work at the frontier of large-scale robot learning: training and fine-tuning the Food Foundation Model, building the data infrastructure that feeds it, and deploying it onto physical robots in production kitchens. You'll bridge research and engineering — translating advances from the latest policy learning, generative modeling, and world model literature into systems that handle real food, with real end effectors, at real throughput. Your models won't just benchmark well; they'll serve millions of meals. We are a small, high-ownership team. We work onsite five days a week and move with startup urgency.
YOUR ROLE AND RESPONSIBILITIES
- Define the architecture, training objectives, and learning approach for the Food Foundation Model — evaluating tradeoffs across generalization, sample efficiency, and deployment constraints
- Investigate and evaluate the latest foundation model architectures — including VLAs, world models, JEPA-style joint embedding models, diffusion policies, and emerging approaches — and assess their applicability to Chef's manipulation and generalization challenges
- Design pre-training, fine-tuning, and alignment pipelines that improve the model's ability to generalize across new food types, kitchen configurations, and end effector types with minimal retraining
- Develop evaluation frameworks that measure real-world generalization and long-horizon reliability — not just offline benchmark accuracy
- Collaborate with the data and platform teams on training data requirements, augmentation strategies, and model serving constraints
- Stay current with the research frontier — reading and critically evaluating recent work from CoRL, RSS, NeurIPS, ICML, and ICLR and forming clear views on what's relevant to production manipulation
BASIC QUALIFICATIONS
- MS or PhD in Machine Learning, Robotics, Computer Science, or a related field — or equivalent industry experience
- 5+ years of experience implementing and deploying ML models for real-world robotics applications
- Hands-on experience with large-scale model training: pre-training, fine-tuning, and post-training alignment pipelines
- Familiarity with modern policy and generative model architectures — diffusion models, transformers, behavior cloning, or large-scale multimodal models
- Strong PyTorch skills and experience building reliable, production-quality training and evaluation infrastructure
- Solid software engineering fundamentals in Python; able to write maintainable code across research and production codebases
- Track record of taking models from research prototype to deployed system on physical hardware
PREFERRED QUALIFICATIONS
- Experience with world models or generative models for robot planning and prediction
- Background in large-scale distributed training (multi-node GPU clusters, FSDP, DeepSpeed)
- Familiarity with simulation environments (MuJoCo, Isaac Sim, Genesis) for synthetic data generation and domain randomization
- Experience deploying models to edge hardware (ONNX, TensorRT, quantization, performance profiling)
- Prior work with contact-rich manipulation, deformable object handling, or food robotics
- Publications at top venues: CoRL, RSS, ICRA, NeurIPS, ICML, ICLR
Chef Robotics is solving one of the hardest problems in AI and robotics — and we ship. Our robots are in production today, generating real data that trains the next generation of food AI. Backed by Kleiner Perkins, Construct, Bloomberg Beta, and Promus Ventures, and built by a team from Cruise, Zoox, Google, Tesla, and Amazon Robotics, we're scaling fast with multiple multi-year enterprise contracts. If you want to build physical AI with real-world deployments and real impact, Chef is the place.
We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.
Chef is an early-stage startup where equity is a major part of the compensation package. Our salary ranges are determined by role, level, and location. The range displayed on each job posting reflects the minimum and maximum target for new hire salaries for the position. Within the range, individual pay is determined by additional factors, including job-related skills, experience, and relevant education or training. In addition to salary and early-stage equity, we offer a comprehensive benefits package that includes medical, dental, and vision insurance, commuter benefits, flexible paid time off (PTO), catered lunch, and 401(k) matching.
See All 1,339+ Senior ML Engineer Jobs
Jump back to the full list of openings and apply to any senior ML engineer role that fits.
Find Senior ML Engineer JobsSenior ML Engineer Job Market
A snapshot from current openings nationwide, updated as new roles post.
Who's Hiring
- Apple96
- Amazon64
- Capital One63
- Nvidia51
- Zoox42
Top Industries Hiring
- Technology & Software521
- Electronics & Hardware142
- Banking & Financial Services99
- Automotive91
- Artificial Intelligence86
What Employers Look For
The qualifications that appear most often in senior ML engineer jobs.
- 5 or more years of hands-on machine learning engineering experience in production environments
- Proficiency in Python and ML frameworks such as PyTorch, TensorFlow, or JAX
- Experience designing and deploying end-to-end ML pipelines at scale
- Familiarity with MLOps tooling including experiment tracking, model registries, and CI/CD for models
- Strong understanding of distributed computing and cloud platforms such as AWS, GCP, or Azure
- Graduate degree in computer science, statistics, or a related quantitative field preferred
Tips for Your Senior ML Engineer Job Search
Quantify model impact on your resume
Recruiters screening senior ml engineer resumes look past model architectures and want outcomes. Replace 'built a classification model' with the latency reduction, accuracy lift, or revenue impact it delivered. Numbers make your contributions defensible in technical screens.
Apply early to roles that fit
Migrate Mate lists senior ml engineer openings from across the United States in one place, so you can find roles that match and apply directly to each listing.
Target job descriptions by deployment environment
Senior ML roles split hard between research-heavy and production-focused. Scan each posting for keywords like 'MLOps', 'model serving', or 'real-time inference' versus 'publications' or 'R&D'. Applying to the wrong side wastes your strongest talking points and theirs.
Prepare a project walkthrough before interviewing
Most senior ml engineer system design rounds ask you to walk through a real project end-to-end. Pick one where you made a consequential architecture decision, owned the tradeoffs, and can explain what you would do differently. Vague project summaries are the most common screen failure.
Negotiate scope before you negotiate compensation
At the senior level, the team size, GPU budget, and model ownership structure affect your actual work more than base pay does. Clarify those in the final round before discussing offer details so you can evaluate whether the role matches your growth goals.
Follow up with a technical observation after interviews
A follow-up email that references a specific technical problem from the interview, adds a detail you did not get to, or proposes an alternative approach signals engagement that generic thank-you notes do not. It also gives the hiring manager something concrete to share with the panel.
Senior ML Engineer Jobs: Frequently Asked Questions
Which companies are hiring the most senior ml engineers?
The companies hiring the most senior ml engineers right now include Apple, Amazon, and Capital One, with the largest share of openings in California, New York, and Washington, based on current listings on Migrate Mate as of June 2026. Demand is concentrated in companies with active model deployment needs rather than early-stage research programs.
How many senior ml engineer jobs are remote?
About 29% of senior ml engineer openings are fully remote or hybrid as of June 2026, making it one of the more remote-accessible senior engineering roles. Sub-areas with the highest remote share include NLP, ranking systems, and applied research, where most collaboration happens asynchronously through code and documentation.
How do you become a senior ml engineer?
Reaching the senior ml engineer level typically requires building and shipping production ML systems, not just research notebooks. Start by owning a model end-to-end at a current employer, from data pipeline to serving infrastructure. Then deepen your MLOps knowledge, contribute to system design decisions, and mentor junior teammates. Documented production impact in your portfolio moves you past resume screens faster than additional credentials.
Can you get a senior ml engineer job with limited experience?
Breaking into senior ml engineer roles with limited direct experience is difficult but possible through adjacent paths. Engineers with strong software engineering backgrounds who have shipped ML-adjacent systems, such as real-time feature stores or data pipelines, are often considered. Demonstrating ownership of a deployed model project, even from a side project or open-source contribution, matters more than years on a resume at many companies.
What does the senior ml engineer interview process look like?
Most senior ml engineer interview processes include a recruiter screen, a take-home or live coding round focused on ML fundamentals, a system design session covering model serving or training infrastructure at scale, and a behavioral round assessing cross-functional collaboration. Some companies also include a research or paper discussion round. The system design round carries the most weight at the senior level and is where most candidates differentiate themselves.
Where can I find and apply to senior ml engineer jobs?
You can find and apply to senior ml engineer jobs on Migrate Mate, which lists current openings from across the United States in one place. Find roles that match your background and apply directly to each listing without being redirected to separate employer portals.
See All 1,339+ Senior ML Engineer Jobs
Jump back to the full list of openings and apply to any senior ML engineer role that fits.
Find Senior ML Engineer Jobs