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, leveraging diverse data sources, and building accurate spatial perception that 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 one of the most sophisticated pieces of hardware on the planet.

How you will make an impact:

• Lead a high-stakes research project focused on either VLA scaling/training or spatial perception (SLAM/Calibration).

• Prototype and deploy your algorithms directly on Atlas, moving from simulation to hardware.

• Architect data pipelines that ingest alternative data sources to improve robot robustness.

• Write production-grade code (Python/C++) that integrates with our existing systems.

• Collaborate across teams To integrate learned policies with low-level robot control.

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 ONE or more of the following tracks:

  • Training large-scale multimodal models (VLMs/LLMs), imitation learning, or generative world models for robotics.

  • Classical and learned SLAM, visual odometry, or extrinsic/intrinsic camera calibration at scale.

Nice to have:

• 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., Open X-Embodiment) or specialized data collection approaches like Universal Manipulation Interface.

• Experience with large-scale cluster training (SLURM, distributed GPU training) and maintaining high-quality codebases.

• Strong grasp of Lie groups, optimization, or transformer architectures.

The hourly pay range for this position is between $30-$45/ 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.