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This role sits at the intersection of machine learning, control systems, and real-world robotics, powering 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 real-world physical constraints — work that directly shapes the future of embodied intelligence.
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.
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 robotics simulation tools such as Gazebo, Isaac Sim, CARLA, MuJoCo, or PyBullet.
Solid background in designing and deploying perception, motion planning, and control pipelines for autonomous systems.
Experience with sensor fusion using camera, LiDAR, and IMU data.
Familiarity with reinforcement learning, imitation learning, or adaptive control techniques.
Ability to evaluate and improve model robustness across varied deployment environments.
Curiosity, grit, and a passion for pushing the boundaries of embodied AI.
Base salary: $220,000–$300,000 USD annually. No visa sponsorship is available for this role; candidates must be eligible to work in the United States without sponsorship.
This is a fully on-site role based in Mountain View, CA. Local candidates or those willing to relocate are encouraged to apply.
