Greenhouse · Ashby · Lever · zero LLM cost for discovery

The job search

Operating System.

500,000+ live roles, scored and ranked before you read them. Tailored CVs from your real proof points. One pipeline from first click to offer.

See how it works →
applyos.app/scan
1Scan
2Track
3Tailor
4Export
5Apply

Relevant Jobs

Scanning Greenhouse (312 new)

Scanning ATS portals0%

Scanning job boards…

Used by people targeting roles at

StripeGoogleMetaAnthropicOpenAIMicrosoftLinearNotionFigmaGitHubAirbnbNetflixAppleVercelDatabricksSalesforceShopifyAtlassianStripeGoogleMetaAnthropicOpenAIMicrosoftLinearNotionFigmaGitHubAirbnbNetflixAppleVercelDatabricksSalesforceShopifyAtlassian

Why applyOS

The old grind vs the smart way

The manual grind

  • Scroll 40 browser tabs looking for roles that might fit
  • Copy-paste the same CV and hope for the best
  • Lose track of where you applied and what happened next
  • Walk into interviews with generic answers to generic questions
  • Evaluate the same duplicate posting from three different sources
  • Miss follow-ups because nothing in your workflow reminds you

With applyOS

  • Scan your target companies' ATS pages — zero LLM cost for discovery
  • Score every role 1–5 across CV Match, North Star, Comp, and Culture
  • One pipeline — 7 status stages, notes per role, nothing falls through
  • Dismiss roles you're not interested in — they move out of your feed
  • Tailor your CV per role — Claude weaves in your matching proof points
  • Surface your top STAR stories, ranked by relevance to each JD

01 — Scan & Score

Every role at your target companies, scored before you read it.

applyOS scrapes your target companies directly via Greenhouse, Ashby, and Lever — zero LLM cost for discovery. Each role is scored 1–5 across four dimensions the moment it's found.

  • CV Match · North Star · Comp · Culture — four named dimensions, not a black box
  • Red flag detection deducts from the score automatically
  • Duplicate detection built in — see each posting exactly once
RelevantNot Interested

47 roles · streaming live

Any4.5+4.0+Remote
St

Stripe

PM, Payments Core

4.9
Li

Linear

Senior Product Manager

4.3
Nt

Notion

Product Lead, AI

3.7
Rp

Rippling

Group PM

3.2

Score breakdown · Stripe PM

CV Match 4/5North Star 5/5Comp 5/5Culture 4/5

Tailor Resume

Stripe · PM, Payments Core

Generated
Loading resume & profile
Analyzing job description
Rewriting with Claude
Saving tailored version
Rewritten / new Unchanged2 stories woven in
## Experience
### Senior Product Manager, Acme Corp
- Led cross-functional launch across payments and identity, shipping to 40 markets in 6 months
- Managed roadmap for 3 product lines across EU and US
- Reduced payment failure rate 18% via cohort analysis and A/B testing of checkout flows
- Collaborated with engineering leads on quarterly planning
Download .docxView diff

02 — Apply & Track

A CV that actually fits the role. Generated in seconds.

Claude rewrites your resume bullets to match the JD — using your real proof points, not invented ones. It weaves in the STAR stories most relevant to this specific role and shows you exactly what changed.

  • Bullets rewritten to match JD language and requirements
  • Your STAR stories woven in where they strengthen the case
  • Diff view shows every changed line — no surprises
  • Log notes per role — interviewers, dates, next steps

03 — Prepare & Win

Your best stories, surfaced for every interview.

Build a STAR story bank once. Before every interview, applyOS reads the JD and surfaces your three most relevant stories — with reasoning for why each one fits.

  • Stories tagged by competency and domain — write once, reuse everywhere
  • Claude reads the JD and ranks your bank by relevance
  • Each story surfaces with a clear reason it fits this specific role

Interview Prep

Stripe · PM, Payments Core

3 stories matched
1Why it fits

JD weights cross-functional influence heavily — this story is your strongest direct match

Cross-functional leadership

Marketplace launch across 40 markets

STAR
2Why it fits

Role calls for data-driven prioritization — cohort analysis story shows quantified impact

Data-driven decisions

Reduced churn 18% via cohort analysis

STAR
3Why it fits

Stage requires 0→1 credibility — team build shows you can operate without a playbook

0→1 product building

Built PM team 0→8 at Series B

STAR

Ranked from your story bank by JD relevance

1–5

structured scoring rubric

4

named scoring dimensions

0

LLM calls for discovery

7

pipeline status stages

Stop managing your
job search.

Start running it.