Reinforcement Learning Engineer

About the role

You will architect and implement sophisticated neural network topologies that directly shape the cognitive capabilities of Apptronik's human-centered robotics platform. In this role, you own the end-to-end development of state-of-the-art reinforcement learning policies that govern dynamic locomotion and manipulation for the Apollo robot. You are responsible for driving performance through the entire development cycle, moving fluidly from high-fidelity simulation prototypes to robust deployment on physical hardware. A core part of your ownership involves optimizing and scaling the RL training pipeline to achieve high-throughput simulation and distributed learning that accelerates iteration speed. You will translate human demonstration data into refined motion retargeting pipelines that provide resilient reference trajectories for learning-based control. Collaboration is central to this position, as you will work tightly with hardware and controls teams to diagnose system-level constraints and co-develop solutions that expand the robot's learned behaviors. You will analyze complex hardware results, synthesize insights, and present findings to guide technical direction and demonstrate progress against key company objectives. By maintaining a high-velocity, ego-free engineering culture, you will share knowledge, participate in rigorous code reviews, and mentor peers to ensure collective success on physical systems.

Key facts

What you'll do

Implement and deploy state-of-the-art reinforcement learning 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 such as mocap and 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.

Maintain a high-velocity, ego-free engineering culture by sharing insights, participating in rigorous code reviews, and mentoring engineers across disciplines.

Partner with simulation and controls experts to validate learning policies in increasingly complex and realistic environments before hardware trials.

Champion best practices in safety, reliability, and reproducibility across data, experiments, and deployed robot behaviors.

Contribute to the design of learning-centric architectures that balance performance, latency, and robustness for real-world robotic applications.

Support the definition and execution of experiments that de-risk technical challenges and provide measurable evidence for commercialization decisions.

Help establish scalable tooling and monitoring frameworks that track policy performance, data quality, and system health across the robotics stack.

Requirements

You hold a PhD degree in Computer Science, Robotics, or a related field, or an MS degree in a similar field with 2+ years of industry experience.

You bring 3+ years of hands-on expertise with common reinforcement learning frameworks such as PyTorch and JAX, coupled with high-fidelity physics simulators like MuJoCo and IsaacGym.

You demonstrate mastery of Python for rapid prototyping and training, alongside strong proficiency in C++ for developing performant, deployable code that interfaces with robotic systems.

You have experience building or utilizing large-scale, distributed training pipelines and possess a strong intuition for their optimization and debugging in production settings.

You have a strong theoretical understanding of modern reinforcement learning, including deep expertise in areas such as imitation learning, model-based RL, and sim-to-real transfer techniques.

You maintain a strong intuition for robot dynamics and controls theory, applying these principles to guide and constrain learning-based approaches in safety-critical contexts.

You have a proven track record of successfully deploying learning-based policies on physical robotic systems, with particular experience in legged robots or manipulators.

You are results-oriented, with a passion for seeing complex algorithms work reliably on real-world hardware and a commitment to rigorous analysis and documentation.

You demonstrate the ability to mentor or provide technical guidance to other engineers within a team environment, fostering knowledge sharing and cross-functional collaboration.

You have a strong publication record in relevant conferences and journals, such as CoRL, RSS, and ICRA, showcasing your contributions to the state of the art in robotics and reinforcement learning.

You are comfortable working in a fast-paced environment where priorities evolve rapidly and you must adapt to changing technical and business requirements.

You communicate clearly and effectively, both in writing and verbally, to convey complex technical concepts to interdisciplinary stakeholders.

You are passionate about human-centered robotics and aligning your technical work with the broader mission of supporting humanity through applied AI.

Nice to have

Experience with commercializing robotics products or working closely with manufacturing and logistics environments.

Familiarity with hardware-in-the-loop testing and real-time control infrastructure.

Knowledge of safety-critical systems engineering practices and functional safety standards relevant to robotics.

Practical notes

Location: Austin, TX

Engagement: Full-time

Travel: None specified