ML Engineer - Robotics

ABOUT THE ROLE This role sits at the intersection of machine learning, control systems, and real-world robotics, building the perception, planning, and decision-making pipelines that make autonomous systems truly adaptive. You'll collaborate with frontier AI researchers and hardware engineers to solve hard, interdisciplinary problems that bridge data-driven learning with physical-world constraints — work that directly shapes the next generation of embodied intelligence. WHAT YOU'LL DO - Develop and optimize ML models for perception, motion planning, and control. - Build computer vision and sensor fusion systems using camera, LiDAR, and IMU data. - Integrate learning-based models with robotics software stacks (ROS/ROS2). - Design pipelines for data collection, simulation, and reinforcement learning workflows. - Collaborate with robotics and hardware engineers to deploy models in live environments. - Continuously evaluate model performance and robustness across diverse real-world scenarios. WHAT WE'RE LOOKING FOR - 3–8 years of professional experience in Machine Learning, Robotics, or Computer Vision. - Proficiency in Python and C++ for robotics and ML development. - Hands-on experience with PyTorch and/or TensorFlow for model development. - Proficiency with ROS or ROS2 and integrating ML models into robotics software stacks. - Experience with simulation and benchmarking environments such as Gazebo, Isaac Sim, CARLA, MuJoCo, or PyBullet. - Solid background in perception, motion planning, and control pipeline design for autonomous systems. - Experience with sensor fusion across camera, LiDAR, and IMU data sources. - Familiarity with reinforcement learning, imitation learning, or adaptive control techniques. - Ability to deploy ML models in real-time or embedded environments. - Must be eligible to work in the United States without employer sponsorship. COMPENSATION & BENEFITS Base salary range: $220,000 – $300,000 USD annually. Visa sponsorship is not available for this role. LOCATION On-site in Mountain View, CA, United States. Local candidates or those willing to relocate are required; remote work is not available for this position.