Job description

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 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. ESSENTIAL DUTIES AND RESPONSIBILITIES or KEY ACCOUNTABILITIES 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. Develop and refine motion retargeting pipelines to translate human demonstration data (mocap, teleoperation) into robust reference trajectories for reinforcement learning. SKILLS AND REQUIREMENTS 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. EDUCATION and/or EXPERIENCE 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. PHYSICAL REQUIREMENTS Prolonged periods of sitting at a desk and working on a computer Must be able to lift 15 pounds at times Vision to read printed materials and a computer screen Hearing and speech to communicate *This is a direct hire. Please, no outside Agency solicitations. 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.

Requirements

Must-haves

ESSENTIAL DUTIES AND RESPONSIBILITIES or KEY ACCOUNTABILITIES
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.
Develop and refine motion retargeting pipelines to translate human demonstration data (mocap, teleoperation) into robust reference trajectories for reinforcement learning.
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.
EDUCATION and/or EXPERIENCE
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
Must be able to lift 15 pounds at times
Vision to read printed materials and a computer screen
Hearing and speech to communicate
This is a direct hire. Please, no outside Agency solicitations.

Nice-to-haves

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

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Keywords to mirror

aipython

Platform: greenhouse
Effort: quick

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