5 months ago
Responsibilities
- Implement and deploy reinforcement-learning algorithms for dynamic locomotion and manipulation tasks on physical humanoid-robot hardware.
- Drive development from simulation prototyping through policy transfer, fine-tuning, and robust deployment on the robot.
- Optimize and scale reinforcement-learning training pipelines and contribute to high-throughput simulation and distributed-training infrastructure.
- Mentor junior engineers through technical guidance, code reviews, and reinforcement-learning best-practice sharing.
- Collaborate with robotics and hardware teams to diagnose system-level issues and enable more complex learned behaviors.
- Analyze and present hardware results to guide technical direction and demonstrate progress against company objectives.
- Develop motion-retargeting pipelines that convert mocap and teleoperation demonstrations into reference trajectories for reinforcement learning.
Requirements
- 5+ years of hands-on experience with reinforcement-learning frameworks such as PyTorch and JAX and high-fidelity physics simulators such as MuJoCo and IsaacGym.
- Mastery of Python and strong proficiency in C++ for prototyping, training, and performant deployable code.
- Experience building or using large-scale distributed training pipelines and optimizing them for faster iteration.
- Deep theoretical understanding of reinforcement learning, including imitation learning, model-based reinforcement learning, and sim-to-real transfer.
- Strong understanding of robot dynamics and controls theory.
- PhD or MS in Computer Science, Robotics, or a related field, with 2+ years of industry experience strongly preferred.
- Proven experience deploying learning-based policies on physical robotic systems, especially legged robots or manipulators.
- Experience mentoring or providing technical guidance to other engineers.
- A strong publication record in relevant conferences or journals such as CoRL, RSS, or ICRA is a significant plus.
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.