About The Role

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. In addition to driving research direction, you will play a key role in shaping the technical roadmap for robot learning at Agility and leveling up a growing team of junior AI research engineers by providing mentorship, guiding project execution, and establishing strong research and engineering practices. 

About the Work

• Establish team-level standards for research execution, including experiment tracking, evaluation protocols, and model benchmarking.
• Mentor and guide junior AI research engineers through project design, experiment execution, and technical problem solving.
• Drive alignment across AI Research and Robotics teams on methods, evaluation, and deployment readiness for learned policies. 
• Review experimental design, code, and results to ensure rigor, reproducibility, and alignment with research goals.
• Help onboard new researchers and accelerate their effectiveness in robot learning and experimental workflows.
• 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.

About You

• 7+ years of total experience in software engineering and/or AI/robotics.
• 3+ years of hands-on experience developing and deploying learning-from-demonstration, reinforcement learning, imitation learning, foundation models, or related robot learning systems in real-world or simulated robotics environments 
• Proven ability to mentor and develop junior engineers or researchers, raising the technical bar of a team.
• Track record of leading complex technical initiatives and influencing technical direction from research through deployment.
• Strong ability to translate ambiguous research problems into structured, executable work for a team.
• Strong programming skills in Python, with proficiency in deep learning frameworks such as PyTorch.
• Experience with modern learning-from-demonstration tools such as DiffusionPolicy.
• Experience with robot data collection, training, and testing on hardware for manipulation tasks.
• MS in Robotics, Computer Science, or a related field.

Bonus Points

• 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++

This a hybrid position based out of one of our Salem, Pittsburgh, or Fremont offices.

The final salary offered to a successful candidate will be dependent on several factors that may include but are not limited to: market location, job-related knowledge, skills, and experience. This range may change based on geographical location and may be modified in the future.