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).
Software Engineering: Proficiency in Python, C++, and PyTorch or JAX, with experience building real-time robotic software stacks.
Data & Evaluation: Experience working with demonstration and teleoperation data, and building evaluation pipelines for learned policies.
Good to Have
Simulation Environments: Experience with physics engines such as IsaacSim, MuJoCo, or Drake for policy training and validation.
Generalist Policies: Experience adapting large, general-purpose manipulation policies to specific tasks and embodiments.
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.
EDUCATION and/or EXPERIENCE
An MS with 3+ years of relevant experience, or a recent PhD, in Robotics, Computer Science, Electrical Engineering, or a related field.
Relevant experience specifically focused on learning-based manipulation or a closely related area.
A proven track record of taking learned policies from a research/simulation environment and successfully deploying them on physical hardware.
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
The annual salary range is $180,000 - $240,000
This is a direct hire. Please, no outside Agency solicitations.
Apptronik provides equal employment opportunities to all employees and applicants for employment and prohibits discrimination and harassment of any type without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state or local laws.
