Senior AI Research Engineer

<div class="content-intro"><p>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.</p></div><h1><span style="font-size: 12pt;"><strong>About The Role</strong></span></h1> <p><span style="font-size: 10pt;">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. You will work on a team, running experiments on humanoid robots, and will research and implement methods which can be transferred into production.</span></p> <h2><span style="font-size: 12pt;"><strong>About the Work</strong></span></h2> <ul> <li style="font-size: 10pt;"><span style="font-size: 10pt;">Design, train, and deploy robust policies for locomotion, manipulation, and dynamic interactions with the environment.</span></li> <li style="font-size: 10pt;"><span style="font-size: 10pt;">Develop core reinforcement learning infrastructure, including scalable training pipelines and evaluation frameworks.</span></li> <li style="font-size: 10pt;"><span style="font-size: 10pt;">Design and implement new simulation environments and tasks to support training and deployment of control policies.</span></li> <li style="font-size: 10pt;"><span style="font-size: 10pt;">Develop, design, and test imitation learning methods</span></li> <li style="font-size: 10pt;"><span style="font-size: 10pt;">Collaborate with Robotics Software and AI engineering teams to develop policies which can be transferred to production</span></li> </ul> <h2><span style="font-size: 12pt;"><strong>About You</strong></span></h2> <ul> <li style="font-size: 10pt;"><span style="font-size: 10pt;">3+ years of experience developing and deploying learning-from-demonstration</span></li> <li style="font-size: 10pt;"><span style="font-size: 10pt;">Strong programming skills in Python, with proficiency in deep learning frameworks such as PyTorch.</span></li> <li style="font-size: 10pt;"><span style="font-size: 10pt;">Experience with modern learning-from-demonstration tools like DiffusionPolicy</span></li> <li style="font-size: 10pt;"><span style="font-size: 10pt;">Experience with robot data collection, training, and testing on hardware to perform manipulation tasks.</span></li> <li style="font-size: 10pt;"><span style="font-size: 10pt;">Ability to work collaboratively in a fast-paced environment to deliver safe, high-quality software</span></li> <li style="font-size: 10pt;"><span style="font-size: 10pt;">MS in Robotics, Computer Science, or a related field.</span></li> </ul> <h2><span style="font-size: 10pt;"><strong>Bonus Points</strong></span></h2> <ul> <li style="font-size: 10pt;"><span style="font-size: 10pt;">PhD in Robotics, Computer Science, or a related field.</span></li> <li style="font-size: 10pt;"><span style="font-size: 10pt;">Publications in top ML or robotics conferences (e.g. NeurIPS, ICML, CoRL, RSS, ICRA).</span></li> <li style="font-size: 10pt;"><span style="font-size: 10pt;">Familiarity with robot simulation environments (e.g. Mujoco, Isaac Sim) and sim-to-real transfer techniques.</span></li> <li style="font-size: 10pt;"><span style="font-size: 10pt;">Experience with modern reinforcement learning techniques for locomotion, manipulation, and whole-body control</span></li> <li style="font-size: 10pt;"><span style="font-size: 10pt;">Experience with writing performant, high quality software in C++</span></li> </ul> <p>This a hybrid position based out of one of our Salem, Pittsburgh, or Fremont offices.</p> <p><em>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.</em></p> <p> </p><div class="content-pay-transparency"><div class="pay-input"><div class="title">Anticipated Salary Range</div><div class="pay-range"><span>$195,000</span><span class="divider">—</span><span>$304,000 USD</span></div></div></div><div class="content-conclusion"><p><strong><em>In addition to base pay, our competitive total rewards package consists of the following for full-time employees:</em></strong></p> <ul> <li><strong>401(k) Plan:</strong><span class="Apple-converted-space"> </span>Includes a 6% company match.</li> <li><strong>Equity:</strong><span class="Apple-converted-space"> </span>Company stock options.</li> <li><strong>Insurance Coverage:</strong><span class="Apple-converted-space"> </span>100% company-paid medic