Software Engineer - Dexterous Manipulation

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.

The Software Engineer – Dexterous Manipulation is a core contributor to our robots' ability to interact with the world with human-like precision. 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 (reinforcement / imitation learning, teleoperation retargeting) into reliable, production-grade software that runs in both simulation and on physical hardware. Working across simulation, hardware, and systems teams, you will get complex manipulation tasks working robustly on real robots.

ESSENTIAL DUTIES AND RESPONSIBILITIES or KEY ACCOUNTABILITIES

• 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.

SKILLS AND 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.

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

EDUCATION and/or EXPERIENCE

• 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