2 months ago
Responsibilities
- Implement and deploy reinforcement-learning algorithms for dynamic locomotion and manipulation tasks on physical hardware.
- Develop policies from simulation prototypes through transfer and fine-tuning on robots.
- Optimize and scale distributed reinforcement-learning training pipelines and supporting infrastructure.
- Develop motion-retargeting pipelines that convert mocap and teleoperation demonstrations into reference trajectories.
- Collaborate with robotics, hardware, and controls teams to diagnose system-level issues and enable complex learned behaviors.
- Analyze and present hardware results to guide technical direction and demonstrate progress.
Requirements
- At least three years of hands-on experience with reinforcement-learning frameworks such as PyTorch or JAX and high-fidelity physics simulators such as MuJoCo or 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 for high-throughput iteration.
- Deep theoretical knowledge of reinforcement learning, including imitation learning, model-based RL, and sim-to-real transfer.
- Strong understanding of robot dynamics and controls theory.
- PhD in Computer Science, Robotics, or a related field, or an MS in a similar field plus at least two years of industry experience.
- 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 preferred.
Benefits
- Direct-hire position
- Equal employment opportunity and anti-discrimination protections are provided
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.