I built an AI that finds and scores my next job

Описание и применение

Пайплайн автоматически собирает, фильтрует и оценивает вакансии, сокращая шум до 12 релевантных предложений в неделю, что повышает эффективность поиска работы для карьерного сменщика.

Задача

Career changers lack tailored job discovery tools that filter for specific constraints and validate postings before application.

Результат

The pipeline processes ~1,500 postings yearly, parking 1,539 and presenting 12 high‑confidence, vetted job opportunities weekly, enabling focused application efforts.

Рабочий процесс

• Collect job postings from 16 APIs, RSS feeds, and Playwright scrapes
• Deduplicate results in a SQLite store
• Apply deterministic hard filters (title, location, recruiter spam, skill experience)
• Score survivors with an LLM against personal constraints
• Verify posting bodies to eliminate false positives
• Manually review and submit applications

Доказательства

Every week my tooling sweeps around twenty job sources and pulls in a few hundred postings.

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Stage 2, hard elimination. Before any model sees anything, cheap deterministic filters kill the obviously wrong postings.

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Stage 3, LLM scoring. The survivors get read by a language model that scores fit against my honest profile.

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Stage 4, verification. Nothing enters the apply queue until the real posting body has been fetched and read.

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I kept waiting for a project impressive enough to prove I could engineer software. At some point I noticed the most honest proof was already running: tested Python, real scrapers, a dedupe store, CI on the ETL framework, and a system in daily use solving a real problem with real stakes.

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