Apptronik
Reinforcement Learning Engineer
About this role
Apptronik seeks a Reinforcement Learning Engineer to develop and deploy state-of-the-art RL algorithms for their humanoid robot Apollo, focusing on locomotion and manipulation tasks. You will drive the full pipeline from simulation to hardware deployment, optimize training infrastructure, and collaborate across robotics and controls teams to achieve world-class physical performance.
What you'll do
- Implement and deploy RL algorithms for dynamic locomotion and manipulation on physical hardware
- Drive full development cycle from simulation prototyping to sim-to-real transfer and policy fine-tuning
- Optimize and scale RL training pipelines for distributed training and faster iteration
- Develop motion retargeting pipelines from human demonstrations (mocap, teleoperation) into RL reference trajectories
- Collaborate with hardware and controls teams to diagnose issues and enable complex learned behaviors
- Analyze hardware results and present findings to guide technical direction
What they're looking for
- Reinforcement learning frameworks (PyTorch, JAX) with 3+ years hands-on experience
- Physics simulators (MuJoCo, IsaacGym)
- Python for rapid prototyping and C++ for performant deployable code
- Large-scale distributed training pipelines and optimization
- Deep RL theory including imitation learning, model-based RL, and sim-to-real transfer
- Robot dynamics and controls theory
- Results-oriented mindset with hardware deployment experience
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Apptronik
Apptronik builds Apollo, a humanoid robot designed for manufacturing, logistics, and other industrial applications. The company is hiring firmware engineers, software engineers, controls specialists, and networking engineers to develop embedded systems, motor control, motion pipelines, and wireless connectivity that power the robot's dexterous manipulation and autonomous operation.
- Website
- apptronik.com
Likely interview questions
- Walk us through a project where you deployed a learned policy on physical robots—what were the biggest sim-to-real gaps you encountered and how did you overcome them?
- Describe your experience optimizing large-scale distributed RL training pipelines. What bottlenecks have you identified and resolved?