Internship – Behavior Machine Learning Engineer, World Models

Bedrock Robotics

Behavior ML intern training world models on fleet data for Bedrock Robotics’ autonomous construction equipment. Connecting model predictions to planning and control on real excavators.

Posted 9/10/2026internshipSan Francisco • California • 🇺🇸 United StatesEntry LevelWebsite

Core Competencies

Role fit

Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates expertise in training and evaluating models using real-world data, particularly in the context of autonomous systems and robotics. Proficient in Python and PyTorch, with a strong foundation in machine learning methodologies and experimental design.

Highest-signal resume keywords

Python ProgrammingModel Training in PyTorchWorld Models ExperienceReinforcement Learning BackgroundRobotics Data Analysis

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Machine LearningModel EvaluationExperimental DesignVideo PredictionSequence Modeling

Soft Skills

Sound Experimental JudgmentCommunication of Findings

Tools & Technologies

Large-Scale Training Infrastructure

Industry Keywords

Autonomous SystemsHeavy Construction EquipmentJob-Site SafetyCritical Infrastructure

Tech Stack

Tools & technologies

PythonPyTorch

About the role

Key responsibilities & impact

Train and evaluate world models on real fleet data collected from autonomous machines in the field
Run architecture explorations and ablations, and make a defensible case for what is working
Build evaluation harnesses and metrics to assess model improvements
Partner with behavior and controls teams to connect model outputs to planning and control
Present findings and provide a foundation for the team to build upon
Work on autonomous systems for heavy construction equipment, improving job-site safety and accelerating critical infrastructure schedules

Requirements

What you’ll need

Currently pursuing a BS, MS, or PhD in computer science, machine learning, robotics, or a related field — or bringing equivalent research or industry experience
Strong Python and hands-on experience training models in PyTorch (or equivalent)
Ability to read a paper and turn it into a working implementation
Comfort with messy, real-world data
Sound experimental judgment and ability to distinguish real results from noise
Experience with world models, video prediction, or sequence modeling (preferred)
Background in reinforcement learning or imitation learning (preferred)
Prior work with robotics or autonomous vehicle data (preferred)
Familiarity with large-scale training infrastructure (preferred)
Legally authorized to work in the United States
Able to commute to the San Francisco office 3–4 days per week
Must be willing to relocate if located outside the work location

Benefits

Comp & perks

Equal Opportunity Employer commitment
Reasonable accommodations during the application or interview process