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Apptronik is a Series B human-centered robotics company developing AI-powered humanoid robots (Apollo) to support humanity across manufacturing, logistics, healthcare, and beyond. As a Reinforcement Learning Engineer, you will be a core technical contributor architecting neural network topologies and implementing state-of-the-art RL algorithms to achieve world-class performance in whole-body locomotion and manipulation on physical hardware.
Key responsibilities include: implementing and deploying advanced RL algorithms for dynamic locomotion and manipulation tasks; driving the full development cycle from simulation prototyping to robust sim-to-real transfer and fine-tuning on physical robots; optimizing and scaling RL training pipelines for faster iteration and high-throughput distributed training; developing motion retargeting pipelines to convert human demonstrations (mocap, teleoperation) into reference trajectories for learning; collaborating with robotics and hardware teams to diagnose system-level issues and co-develop solutions; and analyzing hardware results to guide technical direction.
Required qualifications: 3+ years hands-on expertise with RL frameworks (PyTorch, JAX) and high-fidelity physics simulators (MuJoCo, IsaacGym); mastery of Python for prototyping and C++ for performant deployable code; experience building or optimizing large-scale distributed training pipelines; strong theoretical understanding of modern RL including imitation learning, model-based RL, and sim-to-real transfer; strong intuition for robot dynamics and controls theory; results-oriented mindset with passion for real-world hardware deployment.
Education/experience: PhD in Computer Science, Robotics, or related field, OR MS with 2+ years industry experience; proven track record deploying learning-based policies on physical robotic systems (especially legged robots or manipulators); demonstrated experience mentoring or providing technical guidance to engineers; strong publication record in relevant venues (CoRL, RSS, ICRA) is a plus.
You will join a multidisciplinary team operating at the cutting edge of applied AI, contributing to a high-velocity, ego-free engineering culture with rigorous code reviews and close collaboration across disciplines.
