Description review
Stand | San Francisco, CA | ONSITE
Stand · ONSITE · back to the listing
HR standards
37/100
poor
Title ↔ description
41/100
poor
Reads as
Machine Learning Engineer
99% confident
What this role officially is
data scientist — ESCO, the EU occupation classification
Data scientists find and interpret rich data sources, manage large amounts of data, merge data sources, ensure consistency of data-sets, and create visualisations to aid in understanding data. They build mathematical models using data, present and communicate data insights and findings to specialists and scientists in their team and if required, to a non-expert audience, and recommend ways to apply the data.
Also known as: data scientists, data engineer, research data scientist, data expert, data research scientist
How others title the same work
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Startups
- Attendi | Medior Machine Learning Engineer | Amsterdam, Netherlands | ONSITE (hybrid) | €6,000 - €7,000 per month | Full-time (80–100%, ~4–5 days/week) | Visa sponsorship + 30% ruling possible Attendi
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- Aptura AI | Full-Time | MTS (Applied AI), MTS (SWE / Product) | London | ONSITE / HYBRID Aptura AI
- Arcforma AI (arcforma.ai) | AI Engineer (Marketing / Construction / Arcforma AI (arcforma.ai)
- Staff AI Engineer - Agent Architecture & Behavior Artisan
What the listing never says
- No section describes what the person would actually do. Scope clarity
- No pay range published. Candidates cannot tell whether applying is worth their time. Pay transparency
The listing, marked up
Nothing in the wording of this listing tripped a check. The scores above still judge how complete and coherent it is.
We're building the intelligence layer for physical risk: digital twins of individual homes, run through physics-informed AI World Models, to see exactly how a specific property will perform in a wildfire or hurricane — traditional models only guess at the neighborhood level. That precision lets us do what insurers historically couldn't: measure how much a new roof, cleared vegetation, or better siding actually cuts risk, then price coverage accordingly, turning mitigation into a real financial incentive instead of just advice.
We've scaled to ~$10B in insured assets in just two states in under a year. The interesting problems: ML surrogates that run physics simulations 1,000x+ faster without losing accuracy, structure-level digital twins built from imagery and property data at scale, and systems that reason across risk, cost, and homeowner tradeoffs — we're hiring across engineering and applied science.
Open Roles:
Member of the Technical Staff, AI Harness (Full-time, Hybrid, $270-325k, Offers Equity) - https://www.standinsurance.com/careers/259bb94f-1dfd-458f-88f8-5d1f61c56847/
We've scaled to ~$10B in insured assets in just two states in under a year. The interesting problems: ML surrogates that run physics simulations 1,000x+ faster without losing accuracy, structure-level digital twins built from imagery and property data at scale, and systems that reason across risk, cost, and homeowner tradeoffs — we're hiring across engineering and applied science.
Open Roles:
Member of the Technical Staff, AI Harness (Full-time, Hybrid, $270-325k, Offers Equity) - https://www.standinsurance.com/careers/259bb94f-1dfd-458f-88f8-5d1f61c56847/