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
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Develop and optimize ML models for perception, motion planning, and control.
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Build computer vision and sensor fusion systems using camera, LiDAR, and IMU data.
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Integrate learning-based models with robotics software stacks (ROS/ROS2).
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Design pipelines for data collection, simulation, and reinforcement learning workflows.
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Collaborate with robotics and hardware engineers to deploy models in live environments.
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Continuously evaluate model performance and robustness across diverse real-world scenarios.
What We're Looking For
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3–8 years of professional experience in Machine Learning, Robotics, or Computer Vision.
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Proficiency in Python and C++ for robotics and ML development.
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Hands-on experience with PyTorch and/or TensorFlow for model development.
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Proficiency with ROS or ROS2 and integrating ML models into robotics software stacks.
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Experience with simulation and benchmarking environments such as Gazebo, Isaac Sim, CARLA, MuJoCo, or PyBullet.
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Solid background in perception, motion planning, and control pipeline design for autonomous systems.
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Experience with sensor fusion across camera, LiDAR, and IMU data sources.
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Familiarity with reinforcement learning, imitation learning, or adaptive control techniques.
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Ability to deploy ML models in real-time or embedded environments.
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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.
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