Senior AI Software Engineer, Reinforcement Learning
💰 $120,000 – $180,000/yrMarket estimate · not provided by the employer
At a glance
- Who can apply
- United States
- Work arrangement
- Hybrid · Hybrid- Any Office (Fremont, CA, Salem, OR, or Pittsburgh, PA)
- Employment type
- Full-time
- Seniority
- Senior
- Experience
- 4+ years (as stated by the employer)
- Salary
- $120,000 – $180,000/yr · market estimate
- ✓ Mentions visa sponsorship or relocation support
Checked against the employer's source on October 15, 2026. Details come from the employer's posting; confirm them on the application page.
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Job Description
Agility’s commercially deployed humanoids operate alongside teams in warehouses, manufacturing facilities, and distribution centers—tackling physically demanding and repetitive tasks while enabling workers to focus on higher-value work. With industry-leading safety standards and years of proven deployment data, we're pioneering a new era of automation that enhances human potential.
Agility is a pioneer. Our robot, Digit, is the first to be sold into workplaces across the globe. Our team is differentiated by its expertise in imagining, engineering, and delivering robots with advanced mobility, dexterity, intelligence, and efficiency -- robots specifically designed to work alongside people, in spaces built for people. Every day, we break through engineering challenges and invent new solutions and capabilities that will one day make robots commonplace and approachable. This work is our passion and our responsibility: our mission is to make businesses more productive and people’s lives more fulfilling.
Why Join Now
Digit is one of the only humanoid robots deployed in real customer facilities today—at Schaeffler, GXO, Toyota Motor Manufacturing Canada, and Mercado Libre, with 65,000+ hours of real-world operation logged. In June 2026 we announced plans to become the first pure-play humanoid robotics company to trade publicly (ticker AGLT), in a ~$2.5B merger backed by NVIDIA, Amazon, SoftBank Vision Fund 2, and Foxconn. With $300M+ in orders for Digit and a factory built for 10,000 robots a year, we’re scaling fast—and the policies you build will ship to robots working alongside people in production, not sit in simulation.
About the Role
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.
About You
- 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.
Bonus Qualifications
- 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).
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.
Anticipated Salary Range