Reinforcement Learning Engineer

Apptronik • Austin, TX

2d • Onsite

About The Position

Apptronik is a human-centered robotics company developing AI-powered robots to support humanity in every facet of life. Our flagship humanoid robot, Apollo, is built to collaborate thoughtfully with people, starting with critical industries such as manufacturing and logistics, with future applications in healthcare, the home, and beyond. We operate at the cutting edge of Applied AI, applying our expertise across the full robotics stack to solve some of society's most important problems. You will join a team dedicated to bringing Apollo to market at scale, tackling the complex challenges like safety, commercialization, and mass production to change the world for the better. JOB SUMMARY: As a Reinforcement Learning Engineer, you will be a core contributor to the intelligence and physical capabilities of our humanoid platforms. This role is dedicated to architecting sophisticated neural network topologies and implementing state-of-the-art RL algorithms to achieve world-class performance in whole-body locomanipulation. You will work alongside a multidisciplinary team to develop high-performance policies and optimized training pipelines that allow our robots to move and interact with the world with unprecedented fluidity. Beyond your technical contributions, you will play a key role in maintaining a high-velocity, ego-free engineering culture — sharing insights, participating in rigorous code reviews, and collaborating closely with hardware and controls teams to ensure our collective success on physical hardware.

Requirements

• Hands-on expertise (3+ 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.
• A results-oriented mindset with a passion for seeing complex algorithms work on real-world hardware.

Nice To Haves

• A proven track record of successfully deploying learning-based policies on physical robotic systems, especially legged robots or manipulators.
• Demonstrated experience mentoring or providing technical guidance to other engineers in a team environment.
• A strong publication record in relevant conferences or journals (e.g., CoRL, RSS, ICRA) is a significant plus.

Responsibilities

• Implement and deploy state-of-the-art RL algorithms to achieve ambitious, world-class performance on dynamic locomotion and manipulation tasks with physical hardware.
• Drive the entire development cycle, from prototyping in simulation to robustly transferring and fine-tuning policies on the robot.
• Optimize and scale the RL training pipeline for faster iteration, contributing to core infrastructure for high-throughput simulation and distributed training.
• Develop and refine motion retargeting pipelines to translate human demonstration data (mocap, teleoperation) into robust reference trajectories for reinforcement learning.
• Collaborate closely with the robotics and hardware teams to diagnose system-level issues and co-develop solutions that enable more complex learned behaviors.
• Analyze and present hardware results to guide future technical directions and demonstrate progress on key company objectives.

Benefits

• Apptronik provides equal employment opportunities to all employees and applicants for employment and prohibits discrimination and harassment of any type without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state or local laws.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior