Senior Reinforcement Learning Engineer

The Senior Reinforcement Learning  Engineer is a key hands-on role focused on achieving state-of-the-art performance on our humanoid robots. This engineer will leverage their deep expertise in RL to solve critical locomotion and manipulation challenges and deliver breakthrough results on physical hardware. The primary focus of this role is to rapidly implement iterate and deploy advanced learning algorithms to push the boundaries of what our robots can do. As a senior member of the team this individual will also be responsible for mentoring junior engineers elevating the team's overall technical capabilities through their guidance and expertise.

• 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.
• Mentor junior engineers by providing technical guidance conducting insightful code reviews and sharing best practices in reinforcement learning and software development.
• 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.

• Deep hands-on expertise (5+ 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.

• A PhD or MS in Computer Science Robotics or a related field with 2+ years industry experience strongly preferred.
• 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.

• Prolonged periods of sitting at a desk and working on a computer
• Vision to read printed materials and a computer screen
• Hearing and speech to communicate

What We Do

Apptronik is building robots for the real world to improve human quality of life and to help solve the ever-increasing labor shortage problem. Our team has been building some of the most advanced robots on the planet for years dating back to the DARPA Robotics Challenge. We apply our expertise across the full robotics stack to some of the most important and impactful problems our society faces and expect our products and technology to change the world for the better. We value passion creativity and collaboration to help us overcome existing technological barriers in the industry to create truly innovative products.

Why Work With Us

At Apptronik we don't see a future where man competes against machine. Instead we envision a harmonious world where man and machine coexist. Our mission statement "It is not Man vs. Machine but Man + Machine" encapsulates our belief that the synergy between humans and robots will pave the way for a brighter more advanced future.

Gallery