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Robotics

Software Engineer - Manipulation

Sunnyvale, CAOnsiteFull Time$180,000–$240,000 /yrPosted 10 days agoVisa sponsorship available

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Required skills

  • learning-based manipulation policies
  • post-training methods
  • RL fine-tuning
  • policy distillation
  • interactive imitation learning
  • DAgger
  • teleoperation
  • demonstration collection
  • retargeting
  • dataset aggregation

Nice to have

  • physics engines
  • general-purpose manipulation policies
  • VR interfaces
  • haptic interfaces
  • retargeting algorithms
  • human-in-the-loop control

Role summary

The Software Engineer - Manipulation will develop and deploy learning-based manipulation policies for robots, focusing on post-training methods to enhance performance on physical hardware. This role involves bridging cutting-edge research with scalable production software, working closely with simulation, data, hardware, and systems teams. Responsibilities include implementing state-of-the-art policies, adapting them for new tasks and hardware, building data pipelines, and driving the development cycle from simulation to fine-tuning on robots. The engineer will develop visuomotor policies, define evaluation metrics, and collaborate across teams to ensure reliable, low-latency execution and maintain high codebase standards.

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).
  • 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.

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