About the role
About the role
You will work across the complete learning-to-deployment loop: building representations from human data, training models, evaluating them in simulation and on physical robots, and using failures to decide what to try next. Our research spans vision-language-action models, video models, world models, and policies that learn from biomechanical signals. We are looking for depth in machine learning or robot learning—not expertise in every part of the stack—and the willingness to follow an idea all the way to robot performance.
What you’ll do
- Own pre-train, post-train, and evaluation pipelines of robotics foundation models from diverse multimodal human and robot datasets.
- Develop and adapt vision-language-action models, video models, and world models for dexterous manipulation.
- Investigate representations and training methods for transferring human skills across robot embodiments.
- Build reproducible large-scale training, simulation, and real-world evaluation loops that make research progress measurable.
- Deploy policies on physical robots and diagnose failures across data, perception, learning, control, and hardware.
- Improve practical experiment tooling, including data pipelines, teleoperation, retargeting, visualization, and robot runtime software.
- Read relevant research, reproduce promising ideas, and design focused experiments around the results.
What we’re looking for
- Strong foundations in machine learning, robot learning, computer vision, or a closely related field.
- Experience building and evaluating learning systems in Python using PyTorch, JAX, or similar tools.
- Evidence that you can turn an open-ended technical question into a working experiment and a clear conclusion.
- Comfort debugging real data and physical systems rather than working only with clean benchmarks.
