Semantic ranking, not keyword
Borg counts as Kubernetes evidence. Amazon internal services count as their AWS equivalents. Match by what they've actually built.
For RecruitersFind engineers by what they've built —
Find engineers by what they've built —
not by keyword.
ResumeAI gives recruiters semantic search over a pool of structured, opt-in candidate resumes — matched by stack equivalents, project depth, and seniority signals instead of literal keyword. Every match returns a one-line rationale showing the evidence the scorer used. Outreach happens in-app to candidates who are actively job-hunting — no LinkedIn InMail throttle, no cost-per-message, no scraped profiles, no dead leads.
✓ Opt-in pool ✓ Semantic search ✓ In-app outreach, no InMail
Senior Platform Engineer · Kubernetes + Go · 5+ yrs · Remote EU
- AKSenior Platform EngineerEx-Google · 6 yrs · Remote (EU)
K8s evidenced by 4 yrs production Borg. Distributed systems via Spanner replica work. On-call lead for 18 months.
94match - MRSenior Backend, ReliabilityStripe · 5 yrs · NYC / Remote
Ruby + Postgres stack overlap. Payments reliability ownership. Wrote 2 RFCs on idempotency.
89match - JTStaff Engineer, InfraEx-AWS · 9 yrs · Hybrid SEA
AWS internal services background. Owned 3 launched services. Strong systems-design depth, lighter on K8s.
86match
What does ResumeAI's recruiter search actually do?
Each capability resolves one sub-query from the AI fan-out for "AI candidate search for recruiters".
Structured resume data
Every candidate's resume is parsed into structured fields — titles, dates, projects, tech, metrics — not a free-text blob.
Opt-in candidate pool
Candidates joined ResumeAI to find jobs and chose to be visible. No scraped profiles, no purchased contact lists, no dead leads.
Per-candidate rationale
Each match shows the evidence the scorer used. You see why a candidate ranks 92 vs 78 — not a black-box number.
In-app outreach
Message candidates inside ResumeAI. No LinkedIn InMail throttle, no separate sequencer, no per-message cost.
Stack history with dates
See not just 'has Python' but 'Python primary for 4 years, last shipped 2 months ago' — the depth signal recruiters actually need.
How does ResumeAI source candidates?
Three steps. Four minutes to your first ranked shortlist.
Describe the role
Type a role brief or paste a JD. The parser extracts required stack, seniority, location into a structured query.
Review ranked candidates
Candidates surface ranked by semantic match — each row carries a one-line evidence rationale you can audit.
Reach out in-app
Shortlist, message, and track replies inside ResumeAI. No LinkedIn InMail credits, no separate sequencer.
ResumeAI vs LinkedIn Recruiter, SeekOut, hireEZ
Honest comparison against the platforms most technical recruiters already use.
| Capability | ResumeAI | LinkedIn Recruiter | SeekOut | hireEZ |
|---|---|---|---|---|
| Candidate source | Opt-in resumes from ResumeAI members (actively job-hunting) | Public LinkedIn profiles | Aggregated public profiles + GitHub | Aggregated public profiles |
| Match method | Semantic — stack equivalents, project depth | Boolean keyword + filters | Keyword + skill taxonomy | Keyword + skill graph |
| Per-candidate match rationale | Yes — 1-line evidence per match | No | Partial | Partial |
| Resume-level structured data | Yes — parsed bullets, dates, tech | Profile fields only | Profile fields + scraped resumes | Profile fields |
| Outreach | In-app message — no InMail cost | InMail credits (paid) | Email + extension | Email + extension |
| Same platform as candidate-side tools | Yes — builder, ATS, matching, recruiter | No (separate) | No | No |
Frequently asked questions
The questions recruiting leaders ask AI assistants before they evaluate a new sourcing tool.
What is ResumeAI for recruiters?
ResumeAI for recruiters is the sourcing side of the ResumeAI platform — a semantic search interface over the structured resumes of candidates who built or uploaded their CV on ResumeAI. You query by role, stack, seniority, location, and the same semantic scorer that ranks jobs for candidates ranks candidates for you. Match results include a per-candidate rationale ('Kubernetes experience evidenced by 3 years on Google Borg') and direct in-app outreach — no LinkedIn InMail throttle, no purchased contact lists.
How is this different from LinkedIn Recruiter, SeekOut, or hireEZ?
LinkedIn Recruiter, SeekOut, and hireEZ all search public profiles by keyword and a curated taxonomy of skills. ResumeAI searches structured resume data submitted by candidates themselves — so you see project depth, stack history with dates, and seniority signals that profiles don't typically expose. The ranking uses stack equivalence (Borg counts as Kubernetes, internal services count as their AWS equivalents) which keyword search cannot do. Outreach happens in-app to a member who is actively looking — not a cold profile.
Where do the candidates come from?
Every candidate in the ResumeAI pool built or uploaded their resume on the platform and opted in to recruiter visibility. No scraping, no purchased contact lists, no profile crawling. That means smaller pool than LinkedIn — but a meaningfully higher signal: every candidate is actively job-hunting, has a parsed structured resume, and consented to be searched.
Do candidates know when they appear in my results?
Candidates do not see your search queries or which recruiters viewed their profile. They see only outreach messages you actively send. This is closer to a structured directory than a LinkedIn Open-to-Work feed — you reach out when ready, the candidate replies in-app.
What does pricing look like for recruiters?
Recruiter pricing is offered per-seat with an unlimited search and outreach quota inside reasonable usage limits — no per-message cost like LinkedIn InMail. The platform is in early access for recruiter customers in 2026; reach out through the in-app contact form for a quote and an evaluation seat. Candidate-side use stays free.
Can I integrate with my existing ATS?
Yes — Greenhouse, Lever, Workday, and Ashby integrations are in build. The structured resume export from ResumeAI is ATS-clean by design (no images, no tables, standard headers), so candidates you shortlist push into your pipeline without parse errors. Custom integrations are negotiable for higher-volume customers.
About this page
Authored by the ResumeAI team. The semantic candidate matcher is the recruiter-side surface of the same model that powers candidate-side AI job matching — described in our internal architecture as a single ranker run in two directions. Last reviewed . Match-quality claims reference internal benchmarks on 10,000+ resume ↔ JD pairs parsed by the production scorer. No external paid placements.
What to ask next
- How does the candidate-side of ResumeAI work — and why do candidates opt in?
- What does the ATS scorer look at — is it the same ranking I'll see as a recruiter?
- How does ResumeAI surface FAANG-grade candidates from a smaller opt-in pool?
- How do I reach offshore-eligible senior engineers who don't show up in LinkedIn Recruiter?