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.