Principal Robotics Machine Learning Engineer

Join our team as a Principal Robotics Machine Learning Engineer, where you will be a core contributor to our robot's ability to interact with the world in a human-like manner. You will be responsible for the end-to-end lifecycle of learned manipulation, from data engine design through training to on-robot deployment. This role requires deep fluency in both classical computer vision and modern deep learning, as well as experience in robotic manipulation or complex motion control.

• Contribuer de manière significative à la capacité du robot à interagir avec le monde en utilisant des modèles de monde contextuels semblables à ceux des humains.
• Être responsable de l'ensemble du cycle de vie de l'apprentissage de la manipulation, de la conception du moteur de données à la formation, en passant par le déploiement sur le robot.
• Agir en tant que multiplicateur de force au sein de l'organisation, en favorisant une culture de rigueur technique et en mentorant la prochaine génération de leaders en robotique.

• This requires deep fluency in both classical computer vision and modern deep learning to unlock the full potential of state-of-the-art humanoid robot hardware
• Computer Vision: Familiarity with 6D pose estimation, camera calibration/extrinsics, point cloud processing and visual-servoing
• Hardware Bring-up: Experience with the initial calibration and tuning of high-DOF robotic manipulators
• AI Model Development Proficiency: Expertise in models including CNNs, transformers
• 7+ years of relevant experience (or 5+ years with a PhD) specifically focused on robotic manipulation or complex motion control
• Must be able to lift 15 pounds at times
• Software Engineering: Proficiency in Python, C++, and PyTorch, with experience building real-time robotic software stacks
• Deployment Experience: 5+ years shipping machine learning models on robotic systems in production environments
• Prolonged periods of sitting at a desk and working on a computer
• BS/MS/PhD in Robotics, Computer Science, Electrical Engineering, or a related field
• Hearing and speech to communicate
• A proven track record of taking complex algorithms from conception to successfully deploying them on physical hardware
• AI Data Pipeline Experience: Experience with data collection, annotation, and synthetic data generation for ML
• Vision to read printed materials and a computer screen
• PHYSICAL REQUIREMENTS:
• Teleoperation: Experience with VR/haptic interfaces and retargeting algorithms for human-in-the-loop control
• ML Ops: Familiarity with distributed training and MLOps principles
• Simulation Environments: Experience with physics engines such as IsaacSim, MuJoCo, or Drake for policy training and validation
• Logistics Automation Background: Experience using manipulators to automate dynamic real-world material handling tasks