Reinforcement Learning Engineer

As Lead Reinforcement Learning Engineer, you will define and execute NEURA Robotics' strategy for learning-based humanoid control. You will lead a team of reinforcement learning engineers and researchers, driving the development of next-generation locomotion, manipulation, and whole-body control capabilities for the 4NE1 humanoid platform.

You will be responsible for translating cutting-edge research into robust, production-ready systems that enable autonomous, adaptable, and commercially deployable humanoid robots. Working closely with executive leadership, product teams, controls engineers, perception specialists, and hardware teams, you will shape the future of cognitive robotics at scale.

Your mission & challenges

• Define and drive the strategic roadmap for learning-based humanoid behaviors, spanning reinforcement learning, imitation learning, behavior cloning, and foundation-model-powered robotics.

• Lead the development and deployment of advanced control policies for locomotion, loco-manipulation, and whole-body motion on the 4NE1 humanoid platform.

• Establish scalable frameworks, processes, and quality standards for training, validating, and operating learning-based systems.

• Evaluate, adapt, and industrialize state-of-the-art approaches such as reinforcement learning, imitation learning, offline learning, teleoperation, motion tracking, and robotics foundation models.

• Bridge cutting-edge research and real-world applications by transforming emerging technologies into reliable customer-facing capabilities.

• Spearhead sim-to-real transfer efforts through domain randomization, system identification, physics calibration, actuator modeling, and sensor alignment.

• Ensure robust deployment of learned policies on physical humanoid robots with strong performance, reliability, and safety.

• Build, mentor, and lead a high-performing team of reinforcement learning engineers and roboticists, fostering technical excellence and innovation.

• Provide technical direction and coaching while promoting a culture of ownership, collaboration, and scientific rigor.

• Work closely with Controls, Perception, Platform, Software, and Hardware teams to integrate learning-based capabilities into production robotics systems.

• Align research and engineering initiatives with product goals, business priorities, and long-term company strategy.

• Drive projects from early-stage research through successful deployment, establishing clear success metrics and measurable impact.