2027 Internship Behavior ML Engineer, Learned Manipulation Policies

Bedrock Robotics
San Francisco, CA
On-siteCareer-pivot friendly

Who this role is best for

Geared toward candidates with strong machine learning and robotics backgrounds comfortable with training diffusion policies and evaluating real-world performance.

Best fit for

  • Candidates with academic or industry experience in generative modeling and robot learning
    — “Published work or substantial project experience in diffusion, flow matching, robot learning, or imitation learning”
  • Individuals who can bridge simulation and real-world performance evaluation
    — “Work with the simulation and eval teams to make sure offline metrics actually predict on-machine performance”
  • Candidates who can synthesize complex model behaviors into actionable insights
    — “Run architecture, conditioning, and hyperparameter explorations, and turn the results into clear findings the team can build on”

Things to consider

  • Candidates must be prepared to work with real-world robotics data and evaluate physical-world consequences
    — “The consequences of getting them wrong are measured in cubic yards and bent steel”

How to stand out

  • Highlight experience with diffusion models and behavior cloning in your resume and interviews
    — “Working understanding of generative modeling diffusion models, flow matching, VAEs, or similar and of imitation learning or behavior cloning”
  • Emphasize your ability to interpret real training experiments and communicate results clearly
    — “Experience running and interpreting real training experiments: you know how to tell a real improvement from noise”
  • Showcase prior work on evaluation pipelines or sim-to-real performance analysis
    — “Build and improve evaluation pipelines that score behavior models on task success, smoothness, safety margins, and operator-likeness”
Pace · SteadyCollaboration · HighAutonomy · MediumDecision Impact · Team

Derived from job-description analysis by Serendipath's career intelligence engine.

What success looks like

  • train and iterate on diffusion flow matching
  • build and improve evaluation pipelines
  • investigate failure modes
Typical background
computer scienceroboticsmachine learning

Skills & requirements

Required

Machine LearningDiffusion ModelsFlow MatchingVaesImitation LearningBehavior CloningPythonPyTorchReal Training Experiments

Preferred

Vision Language Action (vla) ModelsReinforcement Learning (RL) PoliciesRobot ManipulationEvaluation In Simulation

Stack & domain

PythonPyTorchDiffusion ModelsFlow MatchingVaesImitation LearningBehavior CloningKubernetesGoReactGrpcPrometheusAlertmanagerGrafanaCommunicationProblem-solvingTeamworkTime ManagementDecision MakingAICloud-nativeLLMComputer VisionSecurityOperations

About the role

Original posting from Bedrock Robotics via Ashby

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

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.

Source: Bedrock Robotics careers (Ashby)

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