VLA Research Intern

Boston Dynamics; Waltham (MA); On-site; USD 41,000 - 96,000; Full time

Job summary

Atlas is seeking PhD-level interns to push the boundaries of Vision-Language-Action (VLA) models for humanoid robots. You will lead research projects and deploy ideas directly onto Atlas hardware, bridging simulation and real-world execution.

The role emphasizes strong experimental rigor, collaboration across teams, and hands-on coding in Python and C++ on real robots. You will work on scaling VLA models with diverse data and training recipes.

Qualifications

• Pursuing PhD or research-focused Master’s in CS/Robotics/ML.
• Experience with imitation learning models for robotics and strong experimental hygiene.
• Ability to deploy algorithms on mobile/humanoid robot platforms.

Responsibilities

• Lead a high-stakes research project establishing hypotheses and experiments for VLA/WAM performance.
• Prototype and deploy data collection, mixing, training recipes, and algorithms on Atlas hardware.
• Collaborate across teams to integrate learned policies with low-level robot control.
• Extend data pipelines to ingest diverse data sources for robustness.
• Write production-grade Python/C++ code that interfaces with existing systems.

Skills

Imitation learning for robotics

Experimentation hygiene

Python/C++ coding

Robotics research

Education

PhD (preferred) or research-focused Master’s in CS/Robotics/ML

Tools

Kubernetes

SLURM

Python

C++

Job description

Are you passionate about the intersection of foundation models and physical AI? As an intern on the Atlas VLA Research Team, you will join a world-class group of engineers and scientists dedicated to giving Atlas the intelligence to perceive, reason, and act.

Our team focuses on scaling Vision-Language-Action (VLA) models that leverage diverse data sources and training recipes to keep Atlas grounded and capable of performing challenging dexterous tasks. We are looking for PhD-level interns to lead research projects that push the boundaries of what humanoid robots can do. You won't work on pure theory; you'll be deploying your ideas directly onto the most sophisticated pieces of hardware on the planet.

How you will make an impact:

• Lead a high-stakes research project establishing hypotheses and experiments for VLA / WAM performance, leveraging and extending our benchmarking, training, and evaluation with various data sources (teleoperation, UMI-style data, VQA, etc.).
• Prototype and deploy your data collection / mixing / grading schemes, pre- / mid- / post-training recipes, and algorithms directly on Atlas, moving from simulation to hardware.
• Collaborate across teams to integrate learned policies with low-level robot control.
• Extend data pipelines that ingest alternative data sources to improve robot robustness.
• Write production-grade code (Python/C++) that integrates with our existing systems, using AI coding agents to accelerate progress and minimize entropy while avoiding AI slop.

We are looking for:

• Actively pursuing a PhD (preferred) or a research-heavy Master’s in Computer Science, Robotics, Machine Learning, or a related field.
• Expertise in training and deploying imitation learning models (VLAs/WAMs) for robotics with strong experimental hygiene and clear knowledge of the state of the art and where the community can improve.

Nice to have:

• Good grasp of machine learning best practices, architectures, and scaling laws among different domains.
• Experience troubleshooting and deploying algorithms on physical robot platforms, especially mobile and humanoid form factors.
• Experience working with and contributing to large-scale datasets (e.g., EgoScale, Open X-Embodiment) or specialized data collection approaches like Universal Manipulation Interface.
• Experience with large-scale cluster training (Kubernetes, SLURM, distributed GPU training) and maintaining high-quality codebases.

The hourly pay range for this position is between $30-$70/ hour. Base pay will depend on multiple individualized factors including, but not limited to internal equity, job related knowledge, skills, education and experience. This range represents a good faith estimate of compensation at the time of posting.