Robot-Learning Engineer: Dynamic Locomotion & Manipulation

The Robot Age; Austin, Northern (TX, KY); Hybrid; USD 120,000 - 180,000; Full time

Job summary

Apptronik is seeking an engineer to advance reinforcement learning for its Apollo humanoid platform in Austin. The role focuses on making learned policies work for whole-body loco-manipulation on physical hardware, spanning the loop from simulation to deployment.

The candidate will implement RL algorithms, transfer policies to real hardware, and collaborate with controls, hardware, and autonomy teams to improve training pipelines and robot behavior.

Qualifications

• 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.

Responsibilities

• 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.

Skills

RL systems

Sim-to-real transfer

Software engineering

Cross-team collaboration

Job description

Apptronik is seeking an engineer to advance reinforcement learning for its Apollo humanoid platform in Austin. The role focuses on making learned policies work for whole-body loco-manipulation on physical hardware, spanning the loop from simulation to deployment.

The candidate will implement RL algorithms, transfer policies to real hardware, and collaborate with controls, hardware, and autonomy teams to improve training pipelines and robot behavior.