Lead Software Engineer - Dexterous Manipulation

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., * 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.
• Technical Leadership & Culture: Act as a force multiplier across the organization. Beyond code reviews, foster a culture of technical rigor, setting the bar for architectural excellence and mentoring the next generation of robotics leaders.

• Dexterous Manipulation: Deep expertise in multi-fingered hand control, grasp planning, and in-hand manipulation, with a strong track record of successful hardware deployment.
• Advanced Control & Learning: Proficiency in learning-based control for robotics (e.g. flow/diffusion-based visiomotor policies, reinforcement learning, reward modeling, etc.).
• Software Engineering: Proficiency in Python, with experience building real-time robotic software stacks.
• Simulation Environments: Experience with physics engines such as IsaacSim, MuJoCo, or Drake for policy training and validation., * Robotic Kinematics: Strong foundation in spatial transformations, Jacobian-based control, and constrained optimization.
• Teleoperation: Experience with VR/haptic interfaces and retargeting algorithms for human-in-the-loop control.
• Tactile Sensing: Experience integrating tactile/haptic feedback into manipulation pipelines.
• Computer Vision: Familiarity with 6D pose estimation, point cloud processing, or visual-servoing.
• Hardware Bring-up: Experience with the initial calibration and tuning of high-DOF robotic end-effectors., * BS/MS/PhD in Robotics, Computer Science, Electrical Engineering, or a related field.
• 5+ years of relevant experience (or 3+ years with a PhD) specifically focused on robotic manipulation or complex motion control.

• A proven track record of taking complex algorithms from a research/simulation environment and successfully deploying them on physical hardware.

• This is a direct hire. Please, no outside Agency solicitations.

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 embodied 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.