Lead 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.

JOB SUMMARY

The Lead Software Engineer - Dexterous Manipulation is a core contributor to our robot’s ability to interact with the world with human-like precision. This role is responsible for leading the development of learning-based dexterous control algorithms that unlock the full potential of state-of-the-art robotic hand hardware.

This role will bridge the gap between cutting-edge research and scalable, reliable production software. Whether leveraging reinforcement learning, imitation learning, teleoperation retargeting, or classical control, they will ensure our robots can perform complex, high-DOF tasks in both simulation and reality. As a technical lead, they will not only design the core software architecture but also influence hardware design to achieve world-class manipulation capabilities.

ESSENTIAL DUTIES AND RESPONSIBILITIES or KEY ACCOUNTABILITIES

• Strategic Ownership: Serve as the technical authority for dexterous manipulation. Create the long-term technical roadmap, ensuring hand control and multi-fingered coordination capabilities outpace industry standards.

• Architectural Definition: Design and enforce the foundational software frameworks for manipulation. Own the decision-making process for balancing autonomous logic with high-fidelity teleoperation, ensuring the architecture is scalable for future hardware generations.

• Research & Innovation: Perform and direct the integration of state-of-the-art research. Select and deploy the specific learning-based policies and vision-integrated systems that will define system physical capabilities.

• Sim-to-Real Ownership: Lead the strategy for high-fidelity simulation. Set the standards for success in virtual environments to optimize policy transitions to physical fleet hardware.

• Hardware Design Influence: Drive the specifications for next-generation hardware. Define the requirements for sensing, degrees of freedom, and torque profiles.

• Production Excellence: Oversee the transition from experimental research to fleet-wide deployment. Ensure performance and reliability of C++/Python code running on production-level assets.