Scrocle

Jobs Engine · 2026

Rank public listings against a profile

Laboratoryscrocle.cloud/demo/jobs-engineIndependent engineer

Jobs Engine interface

A hiring workflow begins with a profile and a large set of listings. Someone must score each one, discard irrelevant results, and record the shortlist, which is slow and error-prone on a spreadsheet.

The constraint

This demo does not scrape LinkedIn, Indeed, or Upwork. It does not call a language model. It does not write to Google Sheets. Listings are a captured sample from Himalayas, Jobicy, and Remotive public feeds.

Built with

  • Python
  • FastAPI

How it works

Profile in. Score the fixture. Rank. Excel out. No apply calls.

The calls that shaped it

Each decision with the pressure that forced it and the price it keeps costing.

  1. Show the score parts

    A single relevance number hides why a listing ranked where it did, and an unexplained shortlist is not reviewable.

    Skills, title, and location are separate bars. If FastAPI is missing, you can see it. A single magic number would hide that.

    The cost: Three score parts to define and tune, where one weight would have been a single number to set.

  2. A captured fixture, never a live scrape

    Public job feeds fail, change shape, and mix in expired listings, so a live fetch would make the page unreliable and the result unreproducible.

    Public APIs fail and mix in junk. A frozen set means the page always has rows. Production would refresh the same feeds on a schedule.

    The cost: The listings are a frozen sample; a production build would refresh the feeds on a schedule.

Open a preset. The list re-ranks, the score parts show why each row scored, and you can download the same rows as Excel. A production build would refresh the public feeds and write the sheet on a schedule. This is the matching and the shortlist without the marking exercise.

Where it stands

Demo. Live at scrocle.cloud/demo/jobs-engine. Profile in, ranked sheet out, on a captured sample of public listings.

  • Profile in: a title, skills, and a location. A ranked shortlist out, with the score parts shown.
  • Listings are a captured sample from the Himalayas, Jobicy, and Remotive public feeds.
  • Matching is keyword overlap across three score parts, and no model is called.
  • The same rows on screen download as a spreadsheet.

What was handed over

  1. How to run the FastAPI demo locally
  2. That matching is keyword overlap across three score parts
  3. What a production refresh and Google Sheet would add

Open the live demo

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