Senior ML Engineer, Foundation Models

Job description

Chef Robotics is bringing AI into the physical world, starting with one of the world’s largest industries: food manufacturing. Food production faces one of the most severe labor shortages in the US, with more than 1 million open jobs today and demand continuing to grow. Our robots help manufacturers automate repetitive food-preparation and assembly tasks so they can increase throughput, improve consistency, and keep production onshore. Today, Chef robots operate in production facilities across North America and Europe, serving customers including Amy’s Kitchen, gategroup, and CookUnity. Our robots have made over 100 million servings in production, creating the world’s largest proprietary dataset for AI-powered manipulation of deformable food. Every meal our robots produce makes the system smarter. Backed by leading investors including Avataar Ventures, Construct Capital, Bloomberg Beta, Promus Ventures, and Kleiner Perkins, we’re scaling rapidly with a robotics-as-a-service (RaaS) model and long-term customer partnerships. Our team includes engineers and leaders from Google, Cruise, Tesla, Amazon Robotics, Dexterity, Bear Robotics, Saildrone, and Zoox, united by a mission to build intelligent machines that solve meaningful problems in the real world. If you’re excited about solving hard problems, creating real customer impact, and shipping systems that operate in production every day, not just in the lab, you’ll feel at home at Chef. **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