Senior ML Engineer, Foundation Models

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

Salary

180,000 – 280,000 USD per year

Chef Robotics is solving one of the hardest problems in AI: bringing intelligence into the physical world.

Our robots are already operating in production facilities every day, generating the real-world data that powers the next generation of embodied AI. If you want to build technology that leaves the lab, ships to customers, and transforms an industry, Chef is the place to do it.

Employment type

Full Time