UCR (Under Control Robotics) builds multipurpose robots to support human workers in the world's toughest jobs—turning dangerous work from a necessity into a choice. Our work demands reliability, robustness, and readiness for the unexpected—on time, every time. We're assembling a mission-driven team focused on delivering real impact in heavy industry, from construction and mining to energy. If you're driven to build rugged, reliable products that solve real-world problems, we'd love to talk.

Ensure all your application information is up to date and in order before applying for this opportunity. Position Overview At UCR, building is a team sport. As a machine learning engineer, you'll take ownership and lead the development of advanced machine learning and AI systems powering multipurpose humanoid robots in the real world. You'll design, implement, and optimize learning algorithms that enable robots to move fluidly across diverse environments while performing complex manipulation tasks. Responsibilities Create and optimize learning pipelines for training locomotion policies that generalize across environments Develop and implement machine learning models for robot perception, decision-making, and motion planning Create and optimize computer vision systems for environmental awareness and object recognition Collaborate with the hardware team to integrate learning systems with the physical platforms Design simulation environments for training and testing learning algorithms Collect, process, and analyze field data to improve learning systems continuously Develop metrics and benchmarks to evaluate robot learning performance Stay current with the latest research and innovations in robotics and machine learning Requirements Master's or PhD in Robotics, Computer Science, Machine Learning, or related field 3+ years of experience developing machine learning applications for robotics systems Strong programming skills in Python, C++, and relevant ML frameworks (TensorFlow, PyTorch) Experience with reinforcement learning, computer vision, and motion planning algorithms Familiarity with ROS (Robot Operating System) or similar robotics frameworks Knowledge of control systems and robot kinematics Strong mathematical background in linear algebra, calculus, and statistics Experience with simulation tools for robotics (Gazebo, MuJoCo, Isaac Sim, etc.) Proven track record of implementing ML solutions in robotics applications Experience with legged robot locomotion and dynamic stability control Nice to Have Experience with humanoid robots or complex multi-joint robotic systems Knowledge of model predictive control (MPC) for locomotion Familiarity with trajectory optimization for legged robots Knowledge of industrial environments (construction, energy, mining, or manufacturing) Understanding of safety considerations for learning-based robotic systems Experience with edge computing and deploying ML models on embedded systems Familiarity with human-robot interaction paradigms Background in imitation learning or learning from demonstration Experience with SLAM (Simultaneous Localization and Mapping) techniques To apply, submit your resume here or email people@ucr.bot . To increase your chances of being selected for an interview, we encourage you to include up to TWO examples of your most representative work featuring hardware demonstrations.

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