Software Engineer - Dexterous Manipulation

About the role

You will join a team dedicated to bringing Apollo, our flagship humanoid robot, to market at scale. As a core contributor, you will implement, tune, and deploy reinforcement learning and related learning-based control for high-DOF, multi-fingered robotic hands—translating state-of-the-art research into reliable, production-grade software that runs in both simulation and on physical hardware.

Responsibilities

• Implement and tune control algorithms for multi-fingered hands—grasping, in-hand manipulation, and tactile-feedback integration.
• Develop and maintain manipulation software with low-latency, reliable execution inside the real-time controls stack.
• Translate state-of-the-art methods (RL / imitation policies, human-to-robot motion retargeting) into production-grade C++/Python.
• Build and refine sim-to-real pipelines—hand models, domain randomization, and validation in IsaacSim / MuJoCo / Drake.
• Deploy and debug manipulation capabilities on physical robots; diagnose sensor-noise, latency, and calibration issues.
• Collaborate with hardware and systems engineers, feeding back on hand performance and sensor/actuator requirements.
• Uphold code quality through rigorous testing, documentation, and peer review.

Requirements

• Dexterous manipulation experience: multi-fingered grasping, in-hand manipulation, and high-DOF hand control.
• Reinforcement learning for robotic control—reward design, training, and debugging—ideally applied to dexterous manipulation (imitation learning and diffusion policies a plus).
• Strong Python and working C++ for real-time robotic software.
• Hands-on experience training and validating policies in a physics simulator (IsaacSim, MuJoCo, or Drake).
• Robotics fundamentals: kinematics, dynamics, and Jacobian-based control.
• A track record of getting algorithms working on physical hardware, not simulation alone.

Qualifications

• BS/MS/PhD in Robotics, Computer Science, Electrical Engineering, or a related field.
• 3+ years of relevant experience in robotic manipulation or complex motion control (recent PhD graduates considered).
• Evidence of taking complex algorithms from research / simulation to successful deployment on physical hardware.

Skills

Nice to have: teleoperation (VR/haptic) and retargeting, tactile-sensing integration, computer vision (6D pose / point clouds), and end-effector bring-up & calibration.

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.