Machine Learning Engineer - Multimodal Modeling
Stand Insurance · San Francisco, CA
$250,000–$295,000
Jul 18, 2026
San Francisco, CA
Jul 21, 2026
What this job asks for AI summary
A hands-on machine learning engineering role focused on designing, training, and deploying multimodal AI systems that combine physical simulation outputs, 3D spatial data, and language models into a unified reasoning platform for property risk assessment. The position covers the full lifecycle — architecture, training, evaluation, and production — and suits engineers with a background in multimodal learning applied to complex physical domains.
Senior level · Remote · Full-time
Pay in the description: $250,000–$295,000
Posted 3 times — it's one opening, so apply once.
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
What the occupation pays Median $122,874 (middle half $87,544–$162,374). This posting is about at that midpoint.
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)
SOC classification is a close call: the role is titled 'Machine Learning Engineer' and emphasizes model architecture, training, and deployment (15-2051 Data Scientists) but also owns end-to-end production systems and ML infrastructure (15-1252 Software Developers). 15-2051 was chosen because the primary day-to-day work is designing, training, and evaluating ML/AI models rather than general software engineering.
The posting names no specific ML frameworks (e.g. PyTorch, TensorFlow) or cloud platforms explicitly — skills were extracted at the capability level as described in the 'Core Skills (Must-Haves)' section.
Physics-informed AI appears in both the required section (as domain experience with 'complex physical systems') and the Nice-to-Haves (surrogate modeling breadth); it is marked required based on its placement in the Core Skills block.
Retrieval/embedding systems, geometric deep learning, and geospatial/remote sensing datasets all appear under 'Nice to Haves' and are marked preferred accordingly.
No minimum years of overall experience are stated; seniority is assessed as Senior based on the end-to-end ownership, production deployment record, and cross-functional scope described.
The posting references San Francisco's Fair Chance Ordinance, suggesting an SF office, but the caller has confirmed this is a fully-remote role with a national candidate pool.
Caller marked this a fully-remote role — scored against the national candidate pool.
Read the full posting
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