Here’s what applying from LinkedIn alone actually looks like: a posting says “2 weeks ago,” it’s been reposted twice, 1,000+ people clicked Apply, and (the part that stings if you’re an international student) paragraph six says “unable to provide visa sponsorship.” You read all of it anyway. Multiply by fifty, every day, forever.
I decided my evenings were worth more than that. So one night I sat down with Claude Code and built JobPilot, a job-search autopilot that runs on GCP while I sleep. Total build time: about 5 hours.
Where the jobs actually come from
The trick is to skip the aggregator lag and go to the source. JobPilot pulls from 7 sources, almost all free:
- Greenhouse, Lever, and Ashby: the ATS boards companies actually post to first. I watch 39 companies directly: Stripe, Anthropic, OpenAI, Databricks, Notion, Ramp, Figma, Datadog, Palantir, Cursor… These APIs are public, keyless, and free.
- Hacker News “Who is Hiring”: free via the Algolia API.
- RemoteOK: free public JSON.
- Adzuna: free API key, capped to postings ≤ 2 days old.
- LinkedIn: yes, it’s still in there, via an Apify scraper actor on free credits, capped at 50 results from the last 24 hours so it never costs real money.
A job posted on a company’s Greenhouse board this morning is in my inbox by the next run. That’s the whole point: I see jobs hours old, not weeks old.
The pipeline (runs 4× a day)
Cloud Scheduler kicks off a Cloud Run job at midnight, 6 AM, noon, and 6 PM ET:
fetch (7 sources) ~100 jobs per source, in parallel
→ filter drop stale, senior, contract, no-sponsorship
→ dedupe SHA1(company|title|location) across sources
→ score Gemini 2.5 Flash, fit 0-100, batches of 10
→ record Google Sheet, 27 columns
→ scan Gmail classify replies → update statuses
→ tailor auto-tailor every job scoring ≥ 60
→ digest one email: today's shortlist, rankedThe dedupe step matters more than it sounds: the same job shows up on LinkedIn, Adzuna, and the company’s own board. JobPilot hashes company + title + location, keeps the highest-fidelity source, and I see it exactly once.
Built for the international-student reality
This is the part no job board does for you. Before any job even reaches the scorer, 13 regex patterns auto-reject the dead ends:
- “US citizenship required” → gone
- ”security clearance / TS-SCI / polygraph / ITAR” → gone
- ”unable to provide visa sponsorship” / “cannot sponsor” → gone
- senior / staff / principal / 5+ years / contract / part-time → gone (I’m early-career; 0 to 2 years only)
Then the scorer double-checks: if the description hints sponsorship is unlikely, the fit score gets capped at 10 and the row is auto-rejected with a note. I never spend ten minutes reading a posting that was never going to work.
Four resumes, tailored while I sleep
I keep 4 master resumes as one-page LaTeX files: SDE, MLE, AI Engineer, and Forward-Deployed Engineer. The scorer picks which variant fits each job, then Gemini tailors it:
- Automatic for anything scoring ≥ 60 (capped at 15 per run).
- One click for everything else, a Tailor button in the dashboard.
- Output: a tailored one-page PDF plus a matching cover letter, compiled with pdflatex, filed into Drive as
{company}_resume.pdf.
Two guardrails I refuse to ship without. First, truth: the model may reorder, rephrase, and re-emphasize, but it may never invent an employer, a date, a metric, or a skill. Second, format: if the PDF spills past one page, it gets recut automatically, and an ATS check gates every resume at ≥ 85% keyword coverage.
Cost per tailored resume + cover letter? About a penny. Gemini 2.5 Flash is absurdly cheap for this.
The application pipeline, tracked
Every job moves through stages in the Sheet: New → Applied → Response → Interview → Offer (or Rejected). The fun part is that the statuses update themselves: a Gmail scanner reads the last 3 days of replies, matches them to tracked applications, and classifies each one: rejection, interview invite, next steps.
Two hard rules: the system never sends an email on its own (outreach drafts land in my Gmail drafts, I hit send), and automation never overwrites my manual edits, it only ever moves a status forward.
The dashboard
A Next.js console on Cloud Run (locked behind IAP, so only I can see it) sitting on top of a Google Sheet that serves as the entire database. Filter by fit, source, status, resume variant; click Tailor; read the “why” the scorer wrote for each match. Plus a morning digest email with the day’s shortlist ranked by fit. Most days I make my apply list from the email alone.
Tech stack & what it costs
- Pipeline: Python 3.12, httpx, pydantic, on a Cloud Run job
- Brain: Gemini 2.5 Flash on Vertex AI (scoring + tailoring + reply classification)
- Resumes: LaTeX + pdflatex in Docker
- Dashboard: Next.js 16 on Cloud Run, behind IAP
- Database: a Google Sheet (genuinely; it’s free, visual, and I can edit it from my phone)
- Glue: Cloud Scheduler, Secret Manager, GitHub Actions auto-deploy on push
| Cost | |
|---|---|
| Setup | $0 |
| Cloud Run + Scheduler + Sheets + Drive | ≈ $0 (free tiers) |
| Gemini 2.5 Flash (scoring ~hundreds of jobs + tailoring daily) | pennies |
| Apify LinkedIn scraper | $0 to $5/mo (free credits) |
| Total | < $5/month |
By the numbers
| LinkedIn alone | JobPilot | |
|---|---|---|
| Job freshness | days to weeks old, reposts | hours old, straight from ATS boards |
| Sponsorship dead ends | read them all yourself | auto-rejected before you see them |
| Resume per application | one generic PDF | 1 of 4 variants, tailored per job, ~1¢ |
| Tracking | a spreadsheet you forget to update | statuses update from your inbox |
| Daily effort | hours of scrolling | read one digest email |
One last detail: the hero image on this post was generated by Nano Banana inside the same GCP project the pipeline runs in. The job hunt is fully on-brand now.
The realization that made me build this: a job search is a pipeline problem, not a willpower problem. Recruiters have ATS systems, dashboards, and automation on their side of the table. There’s no rule that says applicants can’t have the same. Mine just had to cost less than a coffee.
