Senior Reinforcement Learning Engineer

Apptronik Apptronik · Robotics · HQ · Advanced Technologies

Senior Reinforcement Learning Engineer to develop state-of-the-art RL algorithms for humanoid robots' locomotion and manipulation, focusing on deploying policies on physical hardware, optimizing training pipelines, and mentoring junior engineers.

What you'd actually do

  1. Implement and deploy state-of-the-art RL algorithms to achieve ambitious, world-class performance on dynamic locomotion and manipulation tasks with physical hardware.
  2. Drive the entire development cycle, from prototyping in simulation to robustly transferring and fine-tuning policies on the robot.
  3. Optimize and scale the RL training pipeline for faster iteration, contributing to core infrastructure for high-throughput simulation and distributed training.
  4. Mentor junior engineers by providing technical guidance, conducting insightful code reviews, and sharing best practices in reinforcement learning and software development.
  5. Collaborate closely with the robotics and hardware teams to diagnose system-level issues and co-develop solutions that enable more complex learned behaviors.

Skills

Required

  • Deep, hands-on expertise (5+ years) with common RL frameworks (e.g., PyTorch, JAX) and high-fidelity physics simulators (e.g., MuJoCo, IsaacGym)
  • Mastery of Python for rapid prototyping and training, alongside strong proficiency in C++ for developing performant, deployable code.
  • Experience building or utilizing large-scale, distributed training pipelines and a strong intuition for their optimization.
  • A strong theoretical understanding of modern reinforcement learning, including deep expertise in areas like imitation learning, model-based RL, and sim-to-real transfer techniques.
  • A strong intuition for robot dynamics and controls theory, with the ability to apply these principles to guide and constrain learning-based approaches.

Nice to have

  • A PhD or MS in Computer Science, Robotics, or a related field, with 2+ years industry experience strongly preferred.
  • A strong publication record in relevant conferences or journals (e.g., CoRL, RSS, ICRA) is a significant plus.

What the JD emphasized

  • state-of-the-art performance
  • physical hardware
  • rapidly implement, iterate, and deploy advanced learning algorithms
  • push the boundaries
  • mentoring junior engineers
  • technical guidance
  • elevating the team's overall technical capabilities
  • diagnose system-level issues
  • co-develop solutions
  • complex learned behaviors
  • analyze and present hardware results
  • guide future technical directions
  • demonstrate progress
  • motion retargeting pipelines
  • human demonstration data
  • mocap
  • teleoperation
  • robust reference trajectories
  • reinforcement learning
  • RL frameworks
  • PyTorch
  • JAX
  • high-fidelity physics simulators
  • MuJoCo
  • IsaacGym
  • Python
  • C++
  • performant, deployable code
  • large-scale, distributed training pipelines
  • optimization
  • theoretical understanding
  • modern reinforcement learning
  • imitation learning
  • model-based RL
  • sim-to-real transfer techniques
  • robot dynamics
  • controls theory
  • guide and constrain learning-based approaches
  • results-oriented mindset
  • complex algorithms
  • real-world hardware
  • proven track record of successfully deploying learning-based policies on physical robotic systems
  • legged robots
  • manipulators
  • mentoring or providing technical guidance
  • strong publication record

Other signals

  • humanoid robot
  • Apollo
  • manufacturing
  • logistics
  • healthcare
  • embodied AI
  • robotics stack
  • safety
  • commercialization
  • mass production
  • state-of-the-art performance
  • humanoid robots
  • locomotion
  • manipulation
  • physical hardware
  • rapidly implement
  • iterate
  • deploy advanced learning algorithms
  • push the boundaries
  • mentoring junior engineers
  • technical guidance
  • elevating team's technical capabilities
  • robotics and hardware teams
  • system-level issues
  • co-develop solutions
  • complex learned behaviors
  • analyze and present hardware results
  • guide future technical directions
  • demonstrate progress
  • motion retargeting pipelines
  • human demonstration data
  • mocap
  • teleoperation
  • robust reference trajectories
  • reinforcement learning
  • RL frameworks
  • PyTorch
  • JAX
  • high-fidelity physics simulators
  • MuJoCo
  • IsaacGym
  • Python
  • C++
  • performant, deployable code
  • large-scale, distributed training pipelines
  • optimization
  • theoretical understanding
  • modern reinforcement learning
  • imitation learning
  • model-based RL
  • sim-to-real transfer techniques
  • robot dynamics
  • controls theory
  • guide and constrain learning-based approaches
  • results-oriented mindset
  • complex algorithms
  • real-world hardware
  • PhD or MS
  • Computer Science
  • Robotics
  • industry experience
  • proven track record
  • deploying learning-based policies
  • physical robotic systems
  • legged robots
  • manipulators
  • mentoring
  • technical guidance
  • team environment
  • strong publication record
  • CoRL
  • RSS
  • ICRA