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

Build the Path Forward

We are standing up a new Robot Learning team focused on whole-body loco-manipulation for precision tasks in heavy manufacturing.

We are seeking a Senior Machine Learning Engineer (Tech Lead) for this new team. You write code, set the technical direction, make the architectural decisions the team builds on, mentor junior and intermediate engineers, and help shape how the team works.

What You’ll Do

• Set the ML technical direction for the team, architectural choices on perception, reasoning, and action generation; training methodology; data strategy; the path from research bet to deployed capability. As one of the first senior hires, you are designing the approach, not extending it.
• Own the architectural workstreams that define the team's research and engineering bets — multiple core build streams across action-policy learning, world-model-based supervision, and policy-orchestration interfaces.
• Design hybrid physics-ML architectures for the integrated loco-manipulation stack. Manipulation, locomotion, and the whole-body control coupling between them are not separable on legged platforms; sub-millimetre continuous-trajectory precision at the tool requires the whole-body controller to compensate for base motion in real time. Today on fixed bases; tomorrow on mobile platforms. The integration is the hard problem; you own its design.
• Own the cross-functional partnerships with hardware teams, domain experts, customer-facing assurance standards, and upstream / downstream teams. Drive a phased deployment strategy that builds production trust over time.
• Mentor and shape the team, guide junior and intermediate ICs across software, ML, robotics, and perception backgrounds; establish code-quality standards, review practices, and engineering norms; help identify and attract next hires. You write code throughout — this is a tech-lead role, not a step away from the work.

Who You Are

• 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.