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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Software Engineer - Dexterous Manipulation - **Company:** Apptronik Inc. - **Location:** Mountain View, CA, United States - **Experience:** Experienced - **Contract:** Permanent contract - **Skills:** Systems Engineering, Computer Vision, C++ (Programming Language), Software Quality, Software Debugging, Python (Programming Language), Kinematics, Reinforcement Learning, Information Technology, Low Latency - **Published:** August 4, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=44b9fa2a62e97b43 ## About the Role * Dexterous manipulation experience: multi-fingered grasping, in-hand manipulation, and high-DOF hand control. * Reinforcement learning for robotic control-reward design, training, and debugging-ideally applied to dexterous manipulation (imitation learning and diffusion policies a plus). * Strong Python and working C++ for real-time robotic software. * Hands-on experience training and validating policies in a physics simulator (IsaacSim, MuJoCo, or Drake). * Robotics fundamentals: kinematics, dynamics, and Jacobian-based control. * A track record of getting algorithms working on physical hardware, not simulation alone. * Nice to have: teleoperation (VR/haptic) and retargeting, tactile-sensing integration, computer vision (6D pose / point clouds), and end-effector bring-up & calibration., * BS/MS/PhD in Robotics, Computer Science, Electrical Engineering, or a related field. * 3+ years of relevant experience in robotic manipulation or complex motion control (recent PhD graduates considered). * Evidence of taking complex algorithms from research / simulation to successful deployment 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 ## Description The Software Engineer - Dexterous Manipulation is a core contributor to our robots' ability to interact with the world with human-like precision. You will implement, tune, and deploy reinforcement learning and related learning-based control for high-DOF, multi-fingered robotic hands-translating state-of-the-art research (reinforcement / imitation learning, teleoperation retargeting) into reliable, production-grade software that runs in both simulation and on physical hardware. Working across simulation, hardware, and systems teams, you will get complex manipulation tasks working robustly on real robots., * Implement and tune control algorithms for multi-fingered hands-grasping, in-hand manipulation, and tactile-feedback integration. * Develop and maintain manipulation software with low-latency, reliable execution inside the real-time controls stack. * Translate state-of-the-art methods (RL / imitation policies, human-to-robot motion retargeting) into production-grade C++/Python. * Build and refine sim-to-real pipelines-hand models, domain randomization, and validation in IsaacSim / MuJoCo / Drake. * Deploy and debug manipulation capabilities on physical robots; diagnose sensor-noise, latency, and calibration issues. * Collaborate with hardware and systems engineers, feeding back on hand performance and sensor/actuator requirements. * Uphold code quality through rigorous testing, documentation, and peer review. ## Related Videos - [How I built my own intelligent Robot Arm from Scratch](https://www.wearedevelopers.com/videos/100097-how-i-built-my-own-intelligent-robot-arm-from-scratch) - [Swapping Low Latency Data Storage Under High Load](https://www.wearedevelopers.com/videos/746-swapping-low-latency-data-storage-under-high-load) - [Focoos AI: Building the Future of Computer Vision](https://www.wearedevelopers.com/videos/1659-focoos-ai-building-the-future-of-computer-vision) - [Robots are coming into the wild! 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