About the job
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.
Responsibilities
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.
Qualifications
Minimum
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.
Preferred
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++