Job description

JOIN THE TEAM BRINGING AUTONOMY TO THE BUILT WORLD At Bedrock, we're moving AI out of the lab and onto real job sites. Our team includes veterans who helped launch Waymo, scaled Segment to a $3.2B acquisition, and grew Uber Freight to $5B in revenue. We retrofit the heavy equipment contractors already own, starting with excavators, so experienced crews can safely take on more projects and deliver them more predictably. In August 2026, Bedrock-equipped excavators began doing fully autonomous, paid work on live commercial job sites. We've more than doubled our customer base since 2025, and our machines now work with contractors across the West, South, and Midwest. In October 2026, we raised a $600M Series C co-led by CapitalG and Emergence Capital, bringing our total funding to nearly $1B. We're using it to put more autonomous machines to work, build the field and operations teams behind every deployment, and expand into new machine types and job-site tasks. 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 About This Internship This is a fixed-term, project-based internship designed strictly for students (undergraduate or graduate) seeking resume-building experience, hands-on skill development, or industry exposure rather than a direct career launchpad. Candidates will own discrete, short-term projects that can be fully completed within the internship timeframe. Please note that while exceptional interns may occasionally be considered if business needs align, opportunities for full-time conversion are highly limited." Bedrock Robotics is an Equal Opportunity Employer We’re committed to building a diverse and inclusive workplace. We consider all qualified applicants for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, ancestry, age, disability, veteran status, genetic information, or any other protected characteristic. Reasonable Accommodations We want our hiring process to be accessible to everyone. If you need an accommodation to participate in the application or interview process, please let your recruiter know so we can support you.

Requirements

Must-haves

JOIN THE TEAM BRINGING AUTONOMY TO THE BUILT WORLD
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
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
Interest in construction, earthwork, or heavy equipment - no prior experience required

Nice-to-haves

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

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Keywords to mirror

aimlmachine learningpython

Platform: ashby
Effort: quick

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