Description review

Astoria AI | Founding AI Engineer — Agentic Systems | Remote (async) | Full-time | Equity-only pre-seed, $190k-$280k +

Astoria AI · Remote (async) · back to the listing

HR standards

58/100

needs work

Title ↔ description

59/100

needs work

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

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  • 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

equity post-seed

Astoria AI is building an agentic runtime for human potential — a suite of specialized AI agents serving candidates and companies across the entire hiring and talent lifecycle: career strategy, networking intelligence, verified matching, compliance-native hiring, onboarding, and retention.
The hiring market is structurally broken: 44% of resumes contain fabrications, ATS filters reject 88% of qualified candidates, and 27% of posted jobs are ghost jobs — while regulation (EU AI Act, Mobley v. Workday, FCRA suits) is forcing enterprises to rethink hiring AI. We're building the fix.
You'll own the intelligence layer: LLM-powered agentic workflows (tool use, structured outputs, multi-step orchestration), RAG/retrieval pipelines, lightweight ML for classification/ranking/entity matching, and evaluation loops for hallucination risk and output quality.
Looking for: 6+ years professional SW engineering (Python/TypeScript/APIs/DBs), hands-on experience with LLM APIs and agentic workflows, and ideally RAG/embeddings/vector DB experience beyond basic chatbots.
Stage: pre-launch, pre-seed, founding team, equity-only1 until seed round closes (~4-6 months), then competitive salary + refresh grants.
Apply: [email protected] with „HN“ in subject, plus resume or GitHub

How this was produced

Highlights are found by rule, not by a model: each one is a phrase matched at a known position, and every note is a template we wrote. The two scores come from a typed-decision model (Jev) that reads the listing against the official role definition and real listings for the same role, and returns probabilities rather than prose — it never writes any of the words on this page, and never chooses what to highlight.

Deterministic penalty applied to the HR score: 20 points (from 78 before penalties). Reviewed 21 Sep 2026.