Senior Machine Learning Engineer (Tech Lead), Robot Learning, Loco- Manipulation

Ph.D. or Master's degree in Robotics, Mechanical Engineering, Electrical Engineering, Computer Science, or a related field or equivalent experience. - 5+ years of hands-on robot learning experience. You have shipped sim-to-real policies on real robots, across different tasks or platforms. - Demonstrated technical leadership and mentorship. You have made architectural decisions on robot learning systems that others built on, and you have meaningfully shaped the development of more-junior engineers as a tech lead. This can be in academia (leading a lab subgroup, advising students) or industry. - Deep sim-to-real expertise — domain randomisation, system identification, teacher-student distillation, sim-to-online fine-tuning. You can design a transfer strategy for a novel problem. - Full-stack robot learning — fluent across simulation construction, policy training, data collection, real-world deployment, and failure diagnosis. - Physics-informed ML or hybrid control experience — PINNs, neural ODEs, MPC with learned dynamics, process-model-conditioned generation, or similar. - A defensible view on visual-reasoning-centric substrates for grounded spatial / physical reasoning. - Push-back willingness — you can defend a non-obvious architectural commitment under pressure, and change your mind on evidence. - Strong programming skills in Python and C++; production-quality code with reproducibility, testing, and maintainability discipline. - Strong communication skills, able to convey complex technical concepts to a diverse audience. - Demonstrated independent technical authority — you have set technical direction in a tech-lead capacity, leading a research subgroup, owning an architecture across multiple ICs' work, or making the architectural call on a high-profile project. Strongly Preferred:

Edge inference depth (TensorRT, ONNX, edge-class deployment). - Loco-manipulation experience — locomotion, whole-body control, and manipulation on legged platforms (quadrupeds, humanoids). The coupling between the three is the hard problem; direct hands-on experience or a defensible architectural view both count. - Precision manipulation or surgical robotics — sub-mm accuracy tasks. - Flow-matching action-head design at depth — direct experience with the architectural pattern. - Visual self-supervised representation learning experience on robot or 3D-vision tasks. - Multi-skill workflow or hierarchical policy design — skill sequencing, failure detection, control mode transitions. - Experience building ML capability from early stage — first or second ML engineer on a team, or built a research group's infrastructure from scratch. Why You’ll Love Working Here

Daily free lunch to keep you fueled and connected with the team

Flexible PTO so you can take the time you need, when you need it

Comprehensive medical, dental, and vision coverage

6 weeks fully paid parental leave, plus an additional 6–8 weeks for birthing parents (12–14 weeks total)

401(k) retirement plan through Empower

Generous employee referral bonuses—help us grow our team! Who We Are