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Parse a job posting into structured fields

Paste a job ad and get deterministic JSON or Markdown with the role, employer, location, compensation, skills, work mode, warnings, and evidence snippets.

Try:
Parsed job

About this tool

Job posting parser turns a pasted job ad into a structured summary for screening, spreadsheets, applicant tracking cleanup, or salary comparison. It extracts common fields recruiters and candidates look for: title, company, location, salary or compensation range, employment type, remote/hybrid/onsite work mode, experience level, and known skill keywords.

The parser is deterministic and runs locally in the browser. It uses labelled fields (Company:, Location:, Compensation:), common job-header patterns, and a curated skill keyword list. It does not call an LLM, scrape the original job page, infer missing facts, or verify whether the posting is legitimate.

Worked example:

  1. Paste a posting that starts with Senior Backend Engineer, has Company: Acme Analytics, Location: Remote - US / Toronto, and a compensation line.
  2. Choose JSON output and leave evidence enabled.
  3. Copy the parsed fields into a spreadsheet or review checklist, including the evidence lines that explain where title, company, location, and salary came from.

Limits and edge cases: input is capped at 80,000 characters. The parser works best when the title, company, location, and pay lines are visible in the pasted text. Unusual formatting, image-only postings, hidden salary details, or skill names not in the built-in keyword list may produce warnings or missing fields.

FAQ

Does this use AI to interpret the posting?

No. It is a deterministic heuristic parser. That makes the output repeatable and fast, but it also means the tool will not infer unstated facts or understand every possible synonym.

Can it extract salary when the posting says pay is not disclosed?

No. If no compensation line or money-like range is present, the salary field is null and the warnings list says salary or compensation was not found.

How are skills detected?

The parser searches for a curated list of common technical and business skills such as Python, SQL, React, Docker, AWS, Tableau, and Excel. It does not maintain a full occupation taxonomy, so niche tools may need manual review.

Why include evidence snippets?

Evidence snippets make the extraction auditable. They show the source text used for high-value fields, which is useful when cleaning many postings or checking whether a header line was mistaken for a company or location.

Related tools

Developer & Automation Access

Run it from the terminal

Same engine as this page, headless — via the gizza CLI:

gizza tool job-posting-parser 'Senior Backend Engineer
Company: Acme Analytics
Location: Remote - US / Toronto
Compensation: $150,000 - $185,000 USD
Full-time
We need Rust, Python, PostgreSQL, Docker, Kubernetes, AWS, GraphQL and CI/CD experience.'

New to the CLI? Get gizza →

Open it by URL

Pre-fill and auto-run this tool with query parameters — the names match the API/CLI:

https://gizza.ai/tools/job-posting-parser/?posting=Senior%20Backend%20Engineer%0ACompany%3A%20Acme%20Analytics%0ALocation%3A%20Remote%20-%20US%20%2F%20Toronto%0ACompensation%3A%20%24150%2C000%20-%20%24185%2C000%20USD%0AFull-time%0AWe%20need%20Rust%2C%20Python%2C%20PostgreSQL%2C%20Docker%2C%20Kubernetes%2C%20AWS%2C%20GraphQL%20and%20CI%2FCD%20experience.&output=json&include_evidence=true

Machine-readable descriptor: tool.json — title + parameters JSON Schema for agents.