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Robot Learning Engineer — Planning & Manipulation
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Anyware Robotics
Fremont, CA (In Person)
Full-Time
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Job Description
Robot Learning Engineer — Planning & Manipulation Anyware Robotics Fremont, CA Job Details Full-time 18 hours ago Benefits Health insurance 401(k) Paid time off Paid sick time Qualifications AI platforms (beyond public GPTs) Computational framework C++ Machine learning research Applied research Neural networks Machine learning frameworks Python Full Job Description The robots run on AnywareOS, our industrial physical intelligence system. Its architecture is modular and designed to generalize across applications without reprogramming. We are looking for a Robot Learning Engineer to advance the planning and manipulation capabilities within AnywareOS. About Anyware Robotics Anyware Robotics builds general-purpose mobile manipulator robots for industrial applications. Our robots are trusted by customers across logistics, retail, and manufacturing, supporting applications such as truck unloading, mobile palletizing, and machine tending. Description What You'll Do Improve learned policies for robot manipulation that augment and extend our production system's performance envelope Contribute to our manipulation capabilities : data collection from deployed robots, physics simulation environments, policy training, and deployment validation Use production multimodal data — rich sensor streams from real industrial operations across logistics and manufacturing Collaborate with perception and controls engineers to close the loop between scene understanding, navigation, and other capabilities inside AnywareOS Ship models to production — your work will run on robots at customer sites across multiple industries, not just on a sim bench Required Skills MS/PhD in ML, robotics, or related field (or equivalent industry experience shipping learned robotic behaviors) Strong applied ML fundamentals : policy learning (imitation learning, RL, or diffusion policies), neural network architecture design, training infrastructure Experience with robot learning for manipulation or motion : grasp synthesis, motion generation, trajectory optimization with learned components, or sim-to-real transfer Proficiency in Python and ML frameworks ; comfortable with C++ for deployment-critical paths Understanding of classical planning (rule-based or optimization-based planning, task-space control) Demonstrated ability to go from research idea working system Nice to Have Prior work on force/compliance control or contact-rich manipulation Familiarity with ROS2 and real robot deployment pipelines Experience with sim-to-real transfer at scale (domain randomization, system identification) Background in warehouse/logistics robotics or unstructured environment manipulation Publications at RSS, CoRL, ICRA, or NeurIPS robotics workshops Why Anyware An A+