1 month ago
Base Salary
$230k - $260k/yr
Responsibilities
- Implement and deploy reinforcement learning algorithms for dynamic locomotion and manipulation on physical humanoid robot hardware.
- Develop the full learning cycle from simulation prototyping through policy transfer and fine-tuning on the robot.
- Optimize and scale distributed reinforcement learning training pipelines and high-throughput simulation infrastructure.
- Mentor junior engineers through technical guidance, code reviews, and reinforcement learning best practices.
- Collaborate with robotics and hardware teams to diagnose system-level issues and enable complex learned behaviors.
- Analyze and present hardware results to guide technical direction and demonstrate progress.
- Develop motion retargeting pipelines that convert mocap and teleoperation demonstrations into reference trajectories for reinforcement learning.
Requirements
- 5+ years of hands-on expertise with reinforcement learning frameworks such as PyTorch and JAX and physics simulators such as MuJoCo and IsaacGym.
- Mastery of Python and strong proficiency in C++ for prototyping and performant deployable code.
- Experience building or using large-scale distributed training pipelines and optimizing them.
- Deep theoretical understanding of reinforcement learning, including imitation learning, model-based reinforcement learning, and sim-to-real transfer.
- Understanding of robot dynamics and controls theory and their application to learning-based approaches.
- PhD or MS in Computer Science, Robotics, or a related field.
- Proven experience deploying learning-based policies on physical robotic systems, especially legged robots or manipulators.
- Experience mentoring or providing technical guidance to engineers.
- A strong publication record in relevant conferences or journals such as CoRL, RSS, or ICRA is a significant plus.
Benefits
- Annual compensation of $230,000–$260,000 USD.
- Direct-hire position.
- Physical requirements include prolonged desk and computer work, occasional lifting of 15 pounds, and the ability to communicate using vision, hearing, and speech.
Categories
ML EngineeringRobotics
About Apptronik
Apptronik designs and manufactures AI-enabled humanoid robots, led by its Apollo platform, to automate tasks alongside people in manufacturing and logistics, with potential use in healthcare and homes. The privately held company is headquartered in Austin, Texas, and grew out of the University of Texas at Austin’s Human Centered Robotics Lab; its team previously worked on NASA’s Valkyrie robot. Apptronik’s business model centers on selling robots and the software and services needed to deploy them in industrial environments.