Senior ML Engineer, Foundation Models

Job description

Job 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…
• 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

Job requirements

• 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