About the RoleThis 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.
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 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.
Compensation & BenefitsBase 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.
LocationThis is a fully
on-site role based in
Mountain View, CA. Local candidates or those willing to relocate are encouraged to apply.
