Senior AI Software Engineer, AI Controls

Agility Robotics Agility Robotics · Robotics · Remote · Software Engineering

Agility Robotics is seeking a Senior AI Software Engineer specializing in AI Controls to develop and deploy reinforcement learning (RL) policies for their humanoid robot, Digit. The role involves designing, training, and integrating RL policies for locomotion, manipulation, and whole-body control, with a focus on perception-in-the-loop and sim-to-real transfer. The engineer will also contribute to RL infrastructure and collaborate with teams to ship production-quality policies to robots operating in real-world environments.

What you'd actually do

  1. Design, train, and deploy robust RL policies for locomotion, manipulation, whole body control, and dynamic interactions with the environment.
  2. Integrate perception into RL policies to achieve obstacle-aware, collision-free motion, and perceptive manipulation.
  3. Develop and maintain core RL infrastructure, including scalable training pipelines and evaluation frameworks.
  4. Design and implement new simulation environments and tasks to support training and evaluation of control policies.
  5. Collaborate with on-robot software and deployment teams to ship production-quality policies to Digit.

Skills

Required

  • 4+ years of experience developing and deploying RL policies for robotics applications
  • Strong Python skills
  • hands-on experience with a deep learning framework such as PyTorch
  • Experience designing reward functions, tuning hyperparameters, and implementing exploration strategies
  • Experience with perception-in-the-loop control, integrating real-time sensory inputs for reactive or adaptive behaviors
  • Proven experience deploying reinforcement learning policies on real-world bipedal or quadrupedal robots
  • Familiarity with robot simulation environments (e.g. Mujoco-Warp, Isaac) and sim-to-real transfer

Nice to have

  • Advanced degree (MS or PhD) in Robotics, Computer Science, or a related field
  • Experience with contact-rich manipulation, including force-torque or tactile sensing
  • Familiarity with policy distillation (e.g. teacher–student) for transferring state-based policies to perception-driven ones
  • Publications in top ML or robotics conferences (e.g. NeurIPS, ICML, CoRL, RSS, ICRA)

What the JD emphasized

  • deploying RL policies for robotics applications
  • deploying reinforcement learning policies on real-world bipedal or quadrupedal robots
  • ship production-quality policies to Digit

Other signals

  • deploying RL policies on real-world robots
  • shipping production-quality policies to Digit
  • integrating perception into RL policies
  • developing and deploying RL policies for locomotion, manipulation, whole body control