About the Role
We are a Series A AI company based in Mountain View, CA, building high-quality training and post-training data, robust reinforcement learning environments, and intelligent agents that bridge the gap between AI research and real-world execution. Our work spans multimodal data, agentic systems, and physical intelligence — and we collaborate closely with frontier AI labs and enterprises.
As an ML Engineer – Robotics, you will design, train, and deploy intelligent models that power autonomous systems at the intersection of machine learning, control systems, and real-world robotics. You'll build perception, planning, and decision-making pipelines that make machines truly adaptive, solving hard, interdisciplinary problems that combine data-driven learning with real-world physical constraints.
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, real-world environments.
Continuously evaluate model performance and robustness across diverse scenarios and deployments.
What We're Looking For
Required
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 and benchmarking tools such as Gazebo, Isaac Sim, CARLA, MuJoCo, or PyBullet.
Experience designing and deploying perception, motion planning, and control pipelines for autonomous systems.
Experience with sensor fusion using camera, LiDAR, and IMU data.
Experience with data collection pipelines, simulation environments, and reinforcement learning workflows.
Strong ability to evaluate model performance and robustness across diverse real-world scenarios.
Nice to Have
Familiarity with reinforcement learning, imitation learning, or adaptive control techniques.
Background in localization, SLAM, or advanced control systems.
Passion for embodied intelligence and pushing the boundaries of autonomous systems.
Eligibility
Compensation & Benefits
Location
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