User Research

Senior ML Engineer, Manipulation

Build and evaluate manipulation policies for Chef Robotics' food-assembly automation systems.

What the role actually is

Chef Robotics is hiring a senior ML engineer focused on manipulation in San Francisco. The role designs and trains policies for robotic food handling, including imitation learning, reinforcement learning, diffusion policies, and transformer-based action models.

The listing is technical, but it is directly relevant to robot learning: the work turns production food-handling data, teleoperation, and evaluation pipelines into manipulation behavior that can run on real robots.

What you would work on

  • Design and train learned manipulation policies for variable food items and end effectors
  • Build data collection and training pipelines with teleoperation and kinesthetic teaching inputs
  • Evaluate policy architectures such as diffusion policies and action-model transformers
  • Adapt models for production robots handling deformable and inconsistent materials

What they are asking for

  • Robotics, machine learning, or computer science depth at senior engineering level
  • Experience with robot manipulation, visuomotor control, imitation learning, or reinforcement learning
  • Strong PyTorch and production-minded ML system implementation skills
  • Comfort testing learned policies against real-world robot constraints

Why this one is worth a look

Food manipulation is a hard physical AI problem because the objects are inconsistent, deformable, and messy. Chef's production deployments make this a useful signal for anyone watching robot learning move beyond lab tasks into commercial operations.

About Chef Robotics

San Francisco physical AI company building robotic food assembly systems for commercial food production.

Visit Chef Robotics

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