JOB SUMMARYThe 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 REQUIREMENTSTechnical 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.
