Senior Machine Learning Engineer, Chef Robotics

• 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

Desirable

• 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
• 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