Senior AI Research Engineer

Senior AI Research Engineer

Hybrid
- Any Office (Fremont, CA, Salem, OR, or Pittsburgh, PA)

Agility's commercially deployed humanoids operate alongside teams in warehouses, manufacturing facilities, and distribution centers—tackling physically demanding and repetitive tasks while enabling workers to focus on higher-value work. With industry-leading safety standards and years of proven deployment data, we're pioneering a new era of automation that enhances human potential.

The AI innovation team at Agility works on building and deploying next-generation robot foundation models and end-to-end policies on humanoid robots. Your goal will be to develop and test cutting-edge methods for imitation learning and reinforcement learning on humanoid robots, in order to establish the techniques necessary for humanoid robots to perform different real-world tasks. You will work on a team, running experiments on humanoid robots, and will research and implement methods which can be transferred into production.

Design, train, and deploy robust policies for locomotion, manipulation, and dynamic interactions with the environment.

Develop core reinforcement learning infrastructure, including scalable training pipelines and evaluation frameworks.

Design and implement new simulation environments and tasks to support training and deployment of control policies.

Develop, design, and test imitation learning methods

Collaborate with Robotics Software and AI engineering teams to develop policies which can be transferred to production

3+ years of experience developing and deploying learning-from-demonstration

Strong programming skills in Python, with proficiency in deep learning frameworks such as PyTorch.

Experience with modern learning-from-demonstration tools like Diffusion Policy

Experience with robot data collection, training, and testing on hardware to perform manipulation tasks.

Ability to work collaboratively in a fast-paced environment to deliver safe, high-quality software

MS in Robotics, Computer Science, or a related field.

PhD in Robotics, Computer Science, or a related field.

Publications in top ML or robotics conferences (e.g. NeurIPS, ICML, CoRL, RSS, ICRA).

Familiarity with robot simulation environments (e.g. Mujoco, Isaac Sim) and sim-to-real transfer techniques.

Experience with modern reinforcement learning techniques for locomotion, manipulation, and whole-body control

Experience with writing performant, high quality software in C++