Senior ML Engineer — Foundation Models for Robotic Food

The Robot Age; San Francisco (CA); Hybrid; USD 170,000 - 260,000; Full time

Job summary

Chef Robotics in San Francisco is looking for a senior ML engineer to design and optimize foundation models for robotic food assembly. You will shape architecture, training objectives, deployment tradeoffs, and learning strategies for deformable-food manipulation using production robot data.

You will explore VLAs, world models, and generative approaches while balancing generalization with sample efficiency and real-world deployment constraints.

Qualifications

• Strong ML, robotics, or CS background through graduate work or equivalent experience.
• Experience deploying ML models for real-world robotics applications.
• Hands-on experience with large-scale model training and modern robot-learning methods.
• Interest in manipulation problems involving messy, deformable real-world objects.

Responsibilities

• Define architecture and training strategy for Chef's food foundation model.
• Evaluate model families including VLAs, world models, and generative approaches.
• Use production robot data to improve manipulation policies.
• Balance model generalization, sample efficiency, and deployment constraints.

Skills

ML

Robotics

CS background

Education

Graduate degree or equivalent

Job description

What the role actually is

Chef Robotics is hiring a senior ML engineer to work on foundation models for robotic food assembly. The role is centered on model architecture, training objectives, deployment tradeoffs, and learning approaches for deformable-food manipulation.

The listing calls out vision-language-action models, world models, generative approaches, simulation, and real production robot data from Chef's deployed systems.

What you would work on

• Define architecture and training strategy for Chef's food foundation model
• Evaluate model families including VLAs, world models, and generative approaches
• Use production robot data to improve manipulation policies
• Balance model generalization, sample efficiency, and deployment constraints

What they are asking for

• Strong ML, robotics, or computer science background through graduate work or equivalent experience
• Experience implementing and deploying ML models for real-world robotics applications
• Hands-on experience with large-scale model training and modern robot-learning methods
• Interest in manipulation problems involving messy, deformable real-world objects

Why this one is worth a look

Food handling is a difficult manipulation domain because the objects vary constantly and production uptime matters. Chef's pitch is interesting because it pairs a narrow commercial wedge with a growing proprietary manipulation dataset.