Senior / Staff AI Research Engineer, Embodied Systems Lead at RoboForce
Milpitas, CA
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Jul 2, 2026
Milpitas, CA
Jul 21, 2026
What this job asks for AI summary
A senior/staff-level engineering role focused on owning and leading the full onboard embodied AI stack for physical robots — covering inference systems, data collection, teleoperation, and on-robot reinforcement learning. The work centers on deploying and closing the loop between learned models and real-world robot behavior in industrial settings. It suits an experienced engineer with a strong background in end-to-end robotic systems, real-time programming, and ML model deployment on physical hardware.
Staff level · National · Full-time
Advertised as Senior, but the requirements read as Staff.
We read this from the posting text with AI. Skim the description below before ruling yourself out.
How this req sits in the market our data
Rare in this occupation — lead with these, and say what you built with them.
Most people in this occupation already list these. Still required — just not what gets you shortlisted.
What the occupation pays Median $138,970 (middle half $107,524–$175,762).
Estimated from BLS employment for this occupation and area, per-skill prevalence across our listing corpus, and published wage benchmarks — as of Jul 28, 2026. It is a model, not a headcount.
Why we read it this way (5)
The title reads 'Senior / Staff AI Research Engineer, Embodied Systems Lead' — the posting presents itself as Senior-level but the actual scope (technical owner of a full stack, setting architecture direction, mentoring engineers, cross-team influence) is consistent with a Staff-level role.
Degree requirement: the JD lists a Bachelor's or Master's OR a PhD, but explicitly accepts 'significant relevant experience' as a substitute, so no hard degree gate is set.
ML model deployment is listed as a required skill based on the explicit gate: 'Experience integrating and deploying ML models/policies into real-time robotic or autonomous systems — system ownership and building, rather than model training or research.' This is a systems/deployment capability, not a named tool; it is retained because it is a concrete, gated technical competency central to the role.
Teleoperation systems, on-robot reinforcement learning, VLA/imitation learning, and whole-body control all appear under the 'Bonus Qualifications' section and are marked preferred accordingly.
No compensation figures are provided in the posting.
Read the full posting
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