linkme

Scrapes LinkedIn job listings into a local CSV. Run it daily, delete rows you've applied to.

Usage

bun fetch      # scrape and append new jobs to jobs.csv
bun recheck    # show jobs currently filtered out + keyword suggestions

No setup needed. Results are appended to jobs.csv — reruns are safe, already-seen jobs are skipped by ID.

Output

jobs.csv — one row per job:

column	description
date_found	date you ran the fetch
posted_at	date the job was posted on LinkedIn
search	which search config matched it
title	job title
company	hiring company
link	canonical LinkedIn job URL

Open in any spreadsheet app. Delete rows as you apply or dismiss.

How it works

• Paginated scraping — fetches up to 6 pages per search (150 jobs) with a 400 ms delay between pages to avoid rate limiting
• Card-boundary-safe parsing — each job card is isolated before extracting fields, preventing content from adjacent cards bleeding into company or date values
• Language filter — drops German (m/w/d, umlauts, gender-marked vocabulary) and French (H/F, accented characters) job titles automatically
• Software-role filter — requires the title to contain a relevant term (software, developer, data, AI, backend, frontend, devops, platform, architect, tech lead, typescript, python, rails, node.js, ML, cloud, security, SRE, solutions, consultant, applications/system engineer, director, product/IT/engineering manager, staff/principal engineer, CTO, VP engineering, etc.) — eliminates aviation, mechanical, and pharma-process roles that match "Engineer" broadly
• Seniority filter — drops junior, jr., intern, trainee, apprentice, werkstudent, and entry-level titles
• Global deduplication — job IDs are indexed from the existing CSV on startup; jobs you've already seen are never re-added, even if you re-run
• 30-day recency — only fetches jobs posted in the last month (f_TPR=r2592000)

recheck

bun recheck fetches all three search areas in raw mode (bypassing the software-role filter) and lists every job that would be filtered out — useful for spotting false negatives. It also prints a word-frequency table of the most common terms in filtered titles so you can decide what to add to SOFTWARE_TITLE_RE.

Configure searches

Edit config.ts:

export const KEYWORDS = [
  "Software Engineer",
  "Solutions Engineer",
  "Full Stack Engineer",
  "Backend Engineer",
  "Data Engineer",
  "AI Engineer",
  "Applications Engineer",
  "Platform Engineer",
  "DevOps Engineer",
  "Staff Engineer",
  "Principal Engineer",
  "Tech Lead",
  "Developer",
  "Ruby on Rails",
  "Python",
  "Node.js",
  "TypeScript",
  "Artificial Intelligence",
  "Machine Learning",
  "Data Science",
  "AI Consultant",
  "ML Engineer",
  "Director of Engineering",
  "Engineering Manager",
  "Lead Developer",
  "System Engineer",
  "Solution Engineer",
].join(" OR ");

export const searches: SearchConfig[] = [
  {
    label: "Basel (city)",
    keywords: KEYWORDS,
    geoId: "103383283",
    location: "Basel",
    workType: ["onsite", "hybrid"],
    timePosted: "month",
  },
  {
    label: "Basel Metropolitan Area",
    keywords: KEYWORDS,
    geoId: "90009885",
    location: "Basel Metropolitan Area",
    workType: ["onsite", "hybrid"],
    timePosted: "month",
  },
  {
    label: "Basel-Country",
    keywords: KEYWORDS,
    geoId: "107117530",
    location: "Basel-Country",
    workType: ["onsite", "hybrid"],
    timePosted: "month",
  },
];

geoId values for the Basel area:

• 103383283 — Basel (city)
• 90009885 — Basel Metropolitan Area
• 107117530 — Basel-Country

You can also paste any LinkedIn search URL directly as url: instead of building from filters.

SearchConfig also supports jobType (full-time, part-time, contract, temporary, internship) and seniority (internship, entry, associate, mid-senior, director, executive) to pass LinkedIn's native API filters.