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

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About the role

Build the Path Forward At Path Robotics, we’re building the future of embodied intelligence. Our AI-driven systems enable robots to adapt, learn, and perform in the real world closing the skilled labor gap and transforming industries. We go beyond traditional methods, combining perception, reasoning, and control to deliver field-ready AI that is risk-aware, reliable, and continuously improving through real-world use. Big, hard problems are our everyday work, and our team of intelligent, humble, and driven people make the impossible possible together. 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 evide

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