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.Position OverviewAt 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.ResponsibilitiesCreate and optimize learning pipelines for training locomotion policies that generalize across environmentsDevelop and implement machine learning models for robot perception, decision-making, and motion planningCreate and optimize computer vision systems for environmental awareness and object recognitionCollaborate with the hardware team to integrate learning systems with the physical platformsDesign simulation environments for training and testing learning algorithmsCollect, process, and analyze field data to improve learning systems continuouslyDevelop metrics and benchmarks to evaluate robot learning performanceStay current with the latest research and innovations in robotics and machine learningRequirementsMaster's or PhD in Robotics, Computer Science, Machine Learning, or related field3+ years of experience developing machine learning applications for robotics systemsStrong programming skills in Python, C++, and relevant ML frameworks (TensorFlow, PyTorch)Experience with reinforcement learning, computer vision, and motion planning algorithmsFamiliarity with ROS (Robot Operating System) or similar robotics frameworksKnowledge of control systems and robot kinematicsStrong mathematical background in linear algebra, calculus, and statisticsExperience with simulation tools for robotics (Gazebo, MuJoCo, Isaac Sim, etc.)Proven track record of implementing ML solutions in robotics applicationsExperience with legged robot locomotion and dynamic stability controlNice to HaveExperience with humanoid robots or complex multi-joint robotic systemsKnowledge of model predictive control (MPC) for locomotionFamiliarity with trajectory optimization for legged robotsKnowledge of industrial environments (construction, energy, mining, or manufacturing)Understanding of safety considerations for learning-based robotic systemsExperience with edge computing and deploying ML models on embedded systemsFamiliarity with human-robot interaction paradigmsBackground in imitation learning or learning from demonstrationExperience with SLAM (Simultaneous Localization and Mapping) techniquesTo 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. #J-18808-Ljbffr