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Apptronik is a human-centered robotics company developing AI-powered humanoid robots (Apollo) to support humanity across manufacturing, logistics, healthcare, and beyond. The company operates at the cutting edge of applied AI, solving critical challenges in safety, commercialization, and mass production.
As a Senior Reinforcement Learning Engineer, you will be a key hands-on contributor focused on achieving state-of-the-art performance on humanoid robots. Your primary responsibilities include implementing and deploying advanced RL algorithms to solve critical locomotion and manipulation challenges on physical hardware, driving the full development cycle from simulation prototyping to robust policy transfer and fine-tuning on robots, and optimizing the RL training pipeline for faster iteration and high-throughput distributed training.
You will mentor junior engineers, providing technical guidance, code reviews, and best practices in reinforcement learning and software development. You'll collaborate closely with robotics and hardware teams to diagnose system-level issues and co-develop solutions enabling more complex learned behaviors. Additional responsibilities include analyzing and presenting hardware results to guide technical direction, and developing motion retargeting pipelines to translate human demonstration data (mocap, teleoperation) into robust reference trajectories for RL.
Required qualifications include 5+ years of hands-on expertise with RL frameworks (PyTorch, JAX) and high-fidelity physics simulators (MuJoCo, IsaacGym), mastery of Python for prototyping and strong C++ proficiency for deployable code, experience building large-scale distributed training pipelines, strong theoretical understanding of modern RL (imitation learning, model-based RL, sim-to-real transfer), and solid intuition for robot dynamics and controls theory. A PhD or MS in Computer Science, Robotics, or related field with 2+ years industry experience is strongly preferred. Proven track record of deploying learning-based policies on physical robotic systems (especially legged robots or manipulators) and demonstrated mentoring experience are essential. Strong publication record in relevant conferences (CoRL, RSS, ICRA) is a significant plus.
