AI Engineer III, AI Controls

Remote
Agility Robotics
,
91 43rd St
Suite 220
Pittsburgh
,
PA
 
15201
Salary:
-
Application Deadline:
Posted: 
10/30/2025

About the Role

The AI Controls team builds high-rate learned controllers that enable Digit to move robustly, efficiently, and safely in dynamic environments. As an AI Controls Engineer, you will develop and deploy reinforcement learning models for whole-body robot control, integrating perception to enable locally collision-free locomotion and manipulation in real-world environments.

 

About The Work

  • Design, train, and deploy robust reinforcement learning policies for locomotion, manipulation, and dynamic interactions with the environment.
  • Integrate perception inputs into control policies to achieve obstacle-aware, collision-free motion.
  • Develop and maintain 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.
  • Collaborate with robot software and deployment teams to ship production-quality policies to Digit.

About You

  • 3+ years of experience developing and deploying reinforcement learning models for robotics applications.
  • Strong programming skills in Python, with proficiency in deep learning frameworks such as PyTorch.
  • Experience designing reward functions, tuning hyperparameters, and implementing exploration strategies to solve complex control tasks.
  • Proven experience with perception-in-the-loop control, integrating real-time sensory inputs for reactive or adaptive behaviors.
  • Familiarity with robot simulation environments (e.g. Mujoco, Isaac Sim) and sim-to-real transfer techniques.
  • Ability to work collaboratively in a fast-paced environment to deliver safe, high-quality software.

Bonus Qualifications

  • Advanced degree (MS or PhD) in Robotics, Computer Science, or a related field.
  • Experience deploying reinforcement learning policies on real-world bipedal or quadrupedal robots.
  • Experience with C++ for integration of learned controllers into real-time control systems.
  • Publications in top ML or robotics conferences (e.g. NeurIPS, ICML, CoRL, RSS, ICRA).