2027 Internship Behavior ML Engineer, Learned Manipulation Policies

Join the Team Bringing Advanced Autonomy to the Built World

At Bedrock, we're moving AI out of the lab and into the real world. Our team includes veterans who helped launch Waymo, scaled Segment to a $3.2B acquisition, and grew Uber Freight to $5B in revenue. Today, we're deploying autonomous systems on heavy construction equipment across the country, improving safety on job sites and accelerating schedules on critical infrastructure projects.

We're not here debating the future of AI. We're deploying it in the real world. In just two years, we've raised $350M and achieved the first fully autonomous excavator deployments in construction.

This is where algorithms meet steel-toed boots. You'll work alongside construction veterans and world-class engineers to solve physical-world problems that simulations can't touch. If you're ready to do meaningful work on hard problems, we'd love to have you join us.

About the Role & Team

Teaching a 40-ton excavator to move like an expert operator is a very different problem from teaching a car to stay in its lane. The motions are multimodal, there are many good ways to swing, dig, and dump and the consequences of getting them wrong are measured in cubic yards and bent steel.

Bedrock's Behavior ML team builds the learned policies that decide what our machines actually do, and diffusion policies are a central bet: models that can represent the full distribution of expert behavior instead of averaging it into mush.

As our Behavior ML intern, you'll help train those policies and build the evaluation that tells us whether they're genuinely better working alongside two hosts who sit on both the training and evaluation sides of the problem.

What You'll Do

• Train and iterate on diffusion flow matching, and related behavior policies using real fleet demonstration data and simulated rollouts
• Run architecture, conditioning, and hyperparameter explorations, and turn the results into clear findings the team can build on
• Build and improve evaluation pipelines that score behavior models on task success, smoothness, safety margins, and operator-likeness
• Investigate failure modes - distribution shift, mode collapse, out-of-distribution scenes and propose fixes
• Work with the simulation and eval teams to make sure offline metrics actually predict on-machine performance
• Contribute to the shared training codebase with clean, reviewed, reproducible work
• Share results regularly with the behavior, controls, and autonomy teams

What We're Looking for

Required

• Currently pursuing a BS, MS, or PhD in computer science, robotics, machine learning, or a related field or bringing equivalent research or industry experience
• Strong Python and hands-on experience training models in PyTorch (or equivalent)
• Working understanding of generative modeling diffusion models, flow matching, VAEs, or similar and of imitation learning or behavior cloning
• Experience running and interpreting real training experiments: you know how to tell a real improvement from noise
• Clear communication, you can explain what you tried, what happened, and what you'd do next

Preferred

• Published work or substantial project experience in diffusion, flow matching, robot learning, or imitation learning
• Experience training and building Vision Language Action (VLA) models
• Experience training Reinforcement Learning (RL) policies for robot manipulation
• Experience evaluating policies in simulation and reasoning about the sim-to-real gap
• Familiarity with large-scale training infrastructure and experiment tracking tooling
• Exposure to robotics, autonomous vehicles, or other physical-world control problems
• Interest in construction, earthwork, or heavy equipment - no prior experience required