Reinforcement Learning Engineer

What the role actually is

Apptronik is hiring a reinforcement learning engineer for its Apollo humanoid platform in Austin. The role is about making learned policies work for whole-body loco-manipulation on physical hardware.

The listing emphasizes the full loop from simulation prototypes to robot deployment, which makes this a real robot-learning role rather than a generic ML infrastructure position.

• Implement reinforcement learning algorithms for locomotion and manipulation
• Move policies from simulation into tests on physical humanoid hardware
• Improve training pipelines for faster iteration and better transfer
• Work with hardware, controls, and software teams on robot behavior

• Experience implementing reinforcement learning or robot-learning systems
• Comfort with simulation-to-real transfer and physical robot constraints
• Strong software engineering habits around experimentation and code review
• Ability to collaborate across controls, hardware, and autonomy teams

Humanoid job postings can be vague, but this one names the behavior problem directly: dynamic locomotion and manipulation on hardware. That makes it a useful signal for candidates who want the hard part of humanoids, not just platform support.