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.

### In this role, you will:

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

### What You Bring:

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

### Nice-to-have:

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.

Senior ML Engineer, Foundation Models

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