Chef Robotics

Technology

SeniorMLEngineer,Manipulation

$200–300k San Francisco, California, United States FULL TIME
Market Sentiment
HIGH DEMAND

Neural analysis suggests this role is
optimal for Senior candidates.

The Brief

“Senior ML Engineer, Manipulation at Chef Robotics. Skills: ML Engineering, Robotics Manipulation, Robot Learning. Design manipulation policies. Train manipulation policies”

Industry & Context.

Technology
Problems you'll solve

Solving manipulation problems

Eligibility Requirements

Work onsite five days a week

What They're Looking For.

Must Have

MS or PhD in Robotics, ML, CS, 5+ years ML systems robotics manipulation, Deep expertise in 2 of: imitation learning, reinforcement learning, grasp estimation, learned motion generation, PyTorch skills, Production-quality training pipelines, Production-quality evaluation pipelines, Deploy policies to real robotic hardware, Software engineering fundamentals, Write maintainable, well-tested code, Track record owning projects end-to-end

Nice to Have

Experience with VLA models, Experience with diffusion policies, Experience with transformer-based action representations, Familiarity with MuJoCo, Familiarity with Isaac Sim, Familiarity with Genesis, Sim-to-real transfer techniques, Experience with multiple end effector types, Background in food robotics, Background in agriculture robotics, Background in consumer goods robotics, Experience with model compression, Experience with quantization, Experience with TensorRT for edge deployment, Contributions to open robotics datasets, Publications at CoRL, Publications at ICRA, Publications at RSS, Publications at NeurIPS, Experience using Isaac Sim for synthetic data, Experience using Isaac Sim for domain randomization, Experience using Gazebo for synthetic data, Experience using Gazebo for domain randomization

What You'll Do.

Design manipulation policies

Train manipulation policies

Implement policy architectures

Evaluate policy architectures

Adapt policy architectures

Build data collection pipelines

Define evaluation metrics

Define regression benchmarks

Build recovery behaviors

Build fallback behaviors

Validate grasp-to-place performance

How You'll Work.

Team & Collaboration

Platform team; Data team; Perception engineers; Robotics engineers

Process & Methodology

Project ownership

Full Job Description

## Description Chef Robotics is accelerating the deployment of intelligent machines in the physical world, starting with food production — the sector facing the largest labor shortage in the U.S., with 1.14M unfilled jobs today and 3.1M projected by 2030. These roles can't be offshored, making robotics essential to keeping production onshore and strengthening America's manufacturing base. Our AI-powered robots automate food prep and assembly in commercial kitchens and food manufacturing, and have already produced over 110 million meals in production — generating the world's largest proprietary dataset for deformable food manipulation. Backed by investors including Kleiner Perkins, Construct, Bloomberg Beta, and Promus Ventures, and built by a team from Cruise, Zoox, Google, Tesla, and Amazon Robotics, Chef is rapidly scaling with multiple multi-year contracts and a mission to put an intelligent robot in every commercial kitchen. About the Role Chef Robotics is building autonomous robots that work alongside humans in commercial food preparation environments. Manipulation is one of the hardest open problems in robotics — and food makes it harder. Unlike structured industrial parts, food items are deformable, visually similar, irregularly shaped, and behave differently depending on temperature, moisture, and preparation state. At Chef, we're solving this in production, every day, across a growing menu of meal types and preparation configurations. As a Senior ML Engineer, Manipulation, you will own the learning systems that enable our robots to reliably pick, place, and handle diverse food classes using multiple end effectors — from suction grippers to multi-finger hands. You will work end-to-end: from defining data collection strategies and training policies, to deploying and debugging those policies on physical robots in real environments.  We are a small, high-ownership team. We work onsite five days a week and move with startup urgency. ## In this role, you will Des

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