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. The ideal candidate is technically strong, hands-on, and comfortable working in a dynamic startup environment, with a demonstrated ability to go from research idea to production system.
DescriptionWhat 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 12 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
CompensationThe expected salary range for this role is 170k - 220k USD/year, depending on experience and qualifications.
Additional compensation may include equity, benefits, or other incentives, where applicable.
Benefits & Perks- Comprehensive health insurance for you and your family
- Flexible Paid Time Off (PTO)
- Paid sick leave
- 401(k) plan support
- Daily meal credit
