Senior/Principal Forward Deployed Engineer - Applied AI/ML at Luminary
San Mateo, CA
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Jul 6, 2026
San Mateo, CA
Jul 21, 2026
What this job asks for AI summary
This role sits at the intersection of machine learning research and applied delivery, focused on building physics-informed AI models for customers in engineering-heavy industries. Day to day involves selecting and training model architectures — such as neural operators, graph neural networks, and diffusion models — then working with cross-functional teams to get those models running in production engineering workflows. It suits an experienced ML practitioner with a background in scientific computing or engineering simulation who is comfortable both writing production-grade code and engaging directly with customer teams.
Senior level · 5+ years · San Francisco-Oakland-Berkeley, CA · Full-time
“or” means any one of them counts — you don't need all of them.
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
Roughly 860 people in the San Francisco-Oakland-Berkeley, CA area plausibly meet what this posting asks for (data scientists). range 180–1,300
Applicant volume Heavy — This req sits in a large pool with little in its requirements to thin it, and auto-apply tools fire at everything in the occupation. Applying early and leading with the rare skills below is what gets read.
Most people in this occupation already list these. Still required — just not what gets you shortlisted.
What the occupation pays Median $173,851 (middle half $133,298–$217,653).
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 (7)
The title reads 'Senior/Principal' — the posting is explicitly open to both levels, with Principal candidates expected toward the upper end of the 5–10 year range. Advertised seniority is captured as Senior (the lower of the two named levels); the actual scope and experience requirements support a Senior classification.
An advanced degree (MS or PhD) is listed under Preferred Qualifications only, so the degree requirement is set to None.
Engineering simulation domain knowledge (CFD, FEA, EM, thermal) is framed as 'working knowledge … enough to collaborate effectively' rather than a hard gate on a specific tool, so no named simulation platform is emitted as a skill.
PyTorch is named explicitly as the required deep learning framework; 'or equivalent deep learning framework' is acknowledged with common alternatives listed.
PhysicsNeMo and JAX-based scientific ML stacks appear only under Preferred Qualifications with an 'e.g.' qualifier; they are marked preferred accordingly.
No compensation figures are stated in the posting.
Ignored 1 non-technology phrase(s) as skills (responsibilities/concepts, not named tools): foundation model fine-tuning.
Read the full posting
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