HireSignal - NLP Job Posting Intent Classifier That Predicts Which Roles Are Actually Hiring
Recruiters and job seekers waste weeks applying to ghost postings — jobs that are perpetually open because the company is collecting resumes, not hiring. HireSignal runs NLP intent classification on job postings to score the real hiring urgency, detect ghost listings, and surface only roles where a company is actively filling a seat right now.
Difficulty
intermediate
Category
NLP & Text AI
Market Demand
Very High
Revenue Score
8/10
Platform
Web App
Vibe Code Friendly
No
Hackathon Score
🏆 7/10
Validated by Real Pain
— seeded from real developer complaints
On r/recruitinghell and r/cscareerquestions, senior engineers regularly describe applying to the same job postings for months with no response, later discovering the roles were never actively being filled — the company was collecting resumes passively or the headcount was frozen after the posting went live.
What is it?
Job boards are flooded with ghost postings — roles kept open for months with no active hiring intent, used to build resume pipelines or satisfy HR policy. Recruiters and serious job seekers have no signal on which postings are real. HireSignal scrapes public job postings via SerpAPI or LinkedIn job search, runs a fine-tuned text classifier trained on signals like posting age, edit frequency, language urgency, company hiring velocity, and headcount data from LinkedIn to output a 0-100 hiring urgency score. The MVP targets senior tech job seekers and recruiters who pay for the prioritized feed. Buildable today with HuggingFace zero-shot classification, SerpAPI for job scraping, and a simple Next.js dashboard — no custom model training required for V1.
Why now?
The April 2026 tech layoff recovery wave has produced the highest volume of senior engineers in active job search in years, and ghost posting complaints are at peak visibility on r/cscareerquestions and LinkedIn — making urgency scoring the most-requested missing feature from any existing job board.
- ▸Zero-shot NLP classifier scores each job posting on urgency signals including language, recency, edit history, and hiring velocity.
- ▸Ghost posting detector flags roles open more than 45 days with no headcount change at the company.
- ▸Daily digest email with top 15 high-urgency roles matching user skills and location filters.
- ▸Recruiter bulk scoring API that accepts a list of role URLs and returns urgency scores as JSON.
Target Audience
Senior tech job seekers in active search (estimated 800k at any given time in the US) and independent tech recruiters placing 5+ candidates per month.
Example Use Case
Samantha, a senior backend engineer in active job search, filters her daily feed to only the top 20 roles scored above 75 urgency, applies exclusively to those, and lands 4 first-round interviews in 2 weeks instead of the usual 6 weeks of noise.
User Stories
- ▸As a senior engineer in active job search, I want to see a hiring urgency score on every job posting, so that I stop wasting applications on ghost listings that have been open for 4 months. As an independent recruiter, I want to bulk-score a list of open roles by urgency before presenting them to candidates, so that I only pitch positions my candidates will actually hear back from.
- ▸As a job seeker, I want a daily digest of the top 15 high-urgency roles matching my skills, so that I spend 20 minutes per day on job search instead of 3 hours.
Acceptance Criteria
Urgency Scoring: done when every job posting receives a 0-100 score within 2 hours of being scraped. Ghost Detection: done when postings open more than 45 days with flat company headcount receive a ghost warning badge. Daily Digest: done when email arrives by 7am with top 15 scored roles matching user skill profile. Stripe Billing: done when $29/month checkout completes and user gains full dashboard access.
Is it worth building?
$29/month x 100 job seekers = $2,900 MRR. $149/month x 30 recruiters = $4,470 MRR. Combined $7,370 MRR at month 4. Math assumes ProductHunt launch driving 800 signups at 6% conversion.
Unit Economics
CAC: $15 via ProductHunt and r/cscareerquestions organic posts. LTV: $261 (9 months avg job search duration at $29/month). Payback: under 1 month. Gross margin: 85%.
Business Model
SaaS subscription
Monetization Path
Job seekers: $29/month for prioritized feed and daily digest. Recruiters: $149/month for bulk role scoring API and candidate matching alerts.
Revenue Timeline
First dollar: week 4 via beta upgrade. $1k MRR: month 2. $5k MRR: month 4. $10k MRR: month 7.
Estimated Monthly Cost
SerpAPI: $50, Proxycurl: $40, HuggingFace Inference API: $25, Supabase: $25, Vercel: $20, Resend: $10, Stripe fees: $35. Total: ~$205/month at launch.
Profit Potential
Full-time viable at $7k MRR with two user segments monetizing simultaneously.
Scalability
High — recruiter API plan, ATS integrations (Greenhouse, Lever), and recruiter seat licensing are strong V3 plays at $500+/month.
Success Metrics
Week 3: 200 signups on launch. Month 2: 80 paid job seekers and 15 paid recruiters. Month 3: less than 12% monthly churn.
Launch & Validation Plan
Post a Twitter/X poll asking senior engineers how many ghost postings they have applied to in the last job search. If average exceeds 5, ship immediately.
Customer Acquisition Strategy
First customer: post in r/cscareerquestions and r/recruitinghell offering free 60-day access to the first 20 people who DM with their job search story. Ongoing: ProductHunt launch, Twitter/X job search community, newsletter ads in Levels.fyi weekly digest.
What's the competition?
Competition Level
Low
Similar Products
Teal (application tracker, no urgency scoring), Huntr (job board aggregator, no NLP classification), LinkedIn Premium (no ghost detection) — none provide a hiring urgency score before application.
Competitive Advantage
LinkedIn and Indeed have no ghost posting filter. Huntr and Teal track applications but do not score posting urgency. HireSignal is the only tool that tells you before you apply whether the role is real.
Regulatory Risks
SerpAPI and Proxycurl ToS must be reviewed for commercial use of LinkedIn data. GDPR applies if EU job seekers store profile data. LinkedIn has historically sent cease-and-desist letters to scrapers — Proxycurl as a licensed data provider reduces but does not eliminate this risk.
What's the roadmap?
Feature Roadmap
V1 (launch): job scraping, zero-shot urgency scoring, ghost detector, daily digest, job seeker dashboard. V2 (month 2-3): recruiter bulk scoring API, headcount velocity chart, saved search alerts. V3 (month 4+): ATS integration, resume-to-role match scoring, recruiter candidate portal.
Milestone Plan
Phase 1 (Week 1-2): SerpAPI scraping, HuggingFace classifier, Proxycurl integration, urgency scoring pipeline live. Phase 2 (Week 3): dashboard UI, daily digest email, Stripe billing, landing page, beta launch. Phase 3 (Month 2): 80 paid users, recruiter plan launched, ghost detection accuracy tuned.
How do you build it?
Tech Stack
Next.js, HuggingFace Inference API for zero-shot classification, SerpAPI for job posting data, LinkedIn data via Proxycurl API, Supabase, Stripe, Resend — build with Cursor for classifier pipeline, v0 for job feed UI.
Suggested Frameworks
HuggingFace Transformers, scikit-learn, FastAPI
Time to Ship
3 weeks
Required Skills
HuggingFace zero-shot classification, SerpAPI integration, Proxycurl for LinkedIn headcount data, Supabase, Next.js.
Resources
HuggingFace zero-shot classification docs, SerpAPI job search docs, Proxycurl API docs, Supabase quickstart.
MVP Scope
pages/index.tsx, pages/dashboard.tsx (job feed with urgency scores), pages/recruiter.tsx (bulk score upload), api/score-job.ts, lib/job-scraper.ts (SerpAPI), lib/classifier.ts (HuggingFace zero-shot), lib/proxycurl.ts (headcount data), lib/ghost-detector.ts, lib/digest-builder.ts, supabase/schema.sql.
Core User Journey
Sign up -> set skills and location -> receive first scored job digest in 24h -> apply only to top-scored roles -> upgrade to paid after first interview.
Architecture Pattern
User sets skills and location preferences -> daily cron fetches job postings via SerpAPI -> HuggingFace zero-shot classifier scores each posting -> Proxycurl pulls company headcount delta -> ghost detector flags stale roles -> scored feed saved to Supabase -> user views dashboard and receives Resend digest.
Data Model
User has one SkillProfile. SkillProfile has many JobMatches. JobPosting has one UrgencyScore. JobPosting has one GhostFlag. Company has many JobPostings and one HeadcountHistory.
Integration Points
SerpAPI for job posting scraping, Proxycurl for LinkedIn company headcount data, HuggingFace Inference API for zero-shot classification, Supabase for job and user data, Resend for daily digest, Stripe for billing.
V1 Scope Boundaries
V1 excludes: ATS integrations, resume parsing, application auto-fill, mobile app, recruiter candidate matching, custom alert rules.
Success Definition
A senior engineer who discovered HireSignal on ProductHunt lands a first-round interview from a top-10 scored role within 2 weeks of subscribing and renews without prompting.
Challenges
The hardest non-technical problem is convincing job seekers to pay $29/month when they expect career tools to be free. Framing around time saved and interview rate improvement — with a concrete guarantee like triple your interview rate in 30 days or refund — is required to break the free-tool expectation.
Avoid These Pitfalls
Do not build the recruiter API before the job seeker product is validated — two audiences at once splits focus fatally. Do not rely solely on SerpAPI for job data without a Proxycurl headcount layer or ghost detection is superficial. Distribution is harder than classification — spend week 1 in r/cscareerquestions before writing a line of code.
Security Requirements
Supabase Auth with Google OAuth. RLS on user and skill profile tables. SerpAPI and Proxycurl keys stored in Vercel environment variables only. Rate limit scoring endpoint to 30 req/min per user. GDPR: skill profile and email deletion on account close.
Infrastructure Plan
Vercel for Next.js and API routes. Supabase for Postgres and auth. Vercel Cron for daily scraping and digest jobs. Sentry for error tracking. GitHub Actions for CI. All under $210/month at launch.
Performance Targets
Launch: 150 DAU, 2,000 req/day. Dashboard load under 2s. Classification pipeline under 3s per posting. Daily cron completes all active users under 10 minutes total. Digest emails delivered by 7am user local time.
Go-Live Checklist
- ☐Security audit done
- ☐Stripe checkout tested
- ☐Sentry live
- ☐Vercel Analytics configured
- ☐Custom domain with SSL
- ☐Privacy policy and terms published
- ☐10 beta job seekers signed off
- ☐Rollback plan documented
- ☐ProductHunt and r/cscareerquestions launch posts drafted.
How to build it, step by step
1. Run npx create-next-app@latest hiresignal --typescript. 2. Install @supabase/supabase-js, resend, stripe, axios. 3. Create Supabase schema for users, skill_profiles, job_postings, urgency_scores, ghost_flags. 4. Build lib/job-scraper.ts using SerpAPI Google Jobs endpoint to fetch postings by keyword and location. 5. Build lib/classifier.ts using HuggingFace zero-shot with labels like urgent-hire, pipeline-building, evergreen-ghost. 6. Build lib/proxycurl.ts to fetch company employee count delta over 90 days. 7. Build lib/ghost-detector.ts combining posting age, headcount delta, and classifier output into a 0-100 urgency score. 8. Build daily Vercel cron that fetches, scores, and stores new postings per active user profile. 9. Build dashboard page at pages/dashboard.tsx showing job cards sorted by urgency score with ghost warning badges. 10. Deploy to Vercel, wire Resend daily digest, add Stripe $29/month checkout.
Generated
April 13, 2026
Model
claude-sonnet-4-6
Disclaimer: Ideas on this site are AI-generated and may contain inaccuracies. Revenue estimates, market demand figures, and financial projections are illustrative assumptions only — not financial advice. Do your own research before making any business or investment decisions. Technology availability, pricing, and market conditions change rapidly; always verify details independently.