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# AI Software Engineer, Reinforcement Learning

- **Company:** Agility Logistics Corp.
- **Location:** Salem, OR, United States
- **Experience:** Expert
- **Salary:** $187,000.0 - $292,000.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Python (Programming Language), Reinforcement Learning, Pytorch, Transfer Learning, Deep Learning, Information Technology
- **Published:** October 2, 2026
- **Apply:** https://www.juju.com/job/16_7f3f596d2

## About the Role

* 4+ years of experience developing and deploying RL policies for robotics applications.
 * Strong Python skills and hands-on experience with a deep learning framework such as PyTorch.
 * Experience designing reward functions, tuning hyperparameters, and implementing exploration strategies to solve complex control tasks.
 * 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.
 * A collaborative approach and the ability to deliver safe, high-quality software in a fast-paced environment., * 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).

## Description

The AI Controls team builds high-rate learned controllers that let Digit move robustly, efficiently, and safely in dynamic environments. As an AI Controls Engineer, you'll develop and deploy reinforcement learning policies across humanoid locomotion, whole-body control, and manipulation-integrating perception to enable collision-free, perceptive motion in the real world.

About The Work

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


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