Software Engineer - Manipulation

About the role

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 Software Engineer - Manipulation is a core contributor to our robot's ability to interact with the world reliably and precisely. This role is responsible for developing and deploying learning-based manipulation policies, with a focus on post-training methods that adapt and improve those policies on real tasks and physical hardware.

This role will bridge the gap between cutting-edge research and scalable, reliable production software. Working closely with the simulation, data, hardware, and systems teams, this engineer will take manipulation policies from demonstration data through post-training to robust deployment across our fleet.

ESSENTIAL DUTIES AND RESPONSIBILITIES or KEY ACCOUNTABILITIES

- Implement and deploy state-of-the-art learning-based manipulation policies to achieve reliable, world-class performance on real-world tasks with physical hardware.

- Adapt and improve policies for new tasks and hardware using post-training methods such as RL fine-tuning, policy distillation, and interactive imitation learning (e.g., DAgger).

- Build and maintain the data pipelines that support post-training, including teleoperation and demonstration collection, retargeting, dataset aggregation, and curation.

- Drive the development cycle from simulation and offline evaluation to robustly transferring and fine-tuning policies on the robot.

- Develop visuomotor policies for grasping, in-hand manipulation, and whole-arm manipulation, with reliable, low-latency execution on hardware.

- Define evaluation metrics and regression tests for policy performance in simulation and on hardware, and use the results to guide technical decisions.

- Collaborate closely with the simulation, data, hardware, and systems teams to diagnose system-level issues and co-develop solutions.

- Maintain high standards for the codebase through testing, documentation, and peer code reviews.

SKILLS AND REQUIREMENTS

Technical Skills (Must-Have)

- Manipulation: Strong experience in robotic manipulation, including grasping, in-hand or whole-arm control, and high-DOF motion, with a track record of hardware deployment.

- Learning-Based Control: Hands-on experience training and debugging learned policies, including post-training methods such as RL fine-tuning, distillation, and imitation learning (e.g., DAgger).