Hire the engineer,not the resume.
Hyrrd reads real work — commits, PRs, architecture decisions, career trajectory — and surfaces the engineers who will actually thrive in your role. Built for a world where resumes are cheap and signal is everything.
// signal extraction
Reading 47 repositories from github.com/maya-r
Analyzing 1,284 commits, 312 PRs, 86 code reviews
Building tech-stack depth vector across 5 domains
Matching against 12 active roles in your pipeline
Maya R.
Senior Backend Engineer · 6y · Berlin
Strong match. Deep PostgreSQL + distributed systems signal from kafka-connect contributions. Startup ownership pattern across 3 prior roles.
Trusted by engineering-first teams hiring across > 30 stacks
The problem
Hiring engineers is broken — and AI made it worse.
In a world flooded with AI-generated resumes, the real advantage isn't resume writing. It's signal extraction.
Resume spam from AI
Every recruiter inbox is now flooded with AI-polished, keyword-stuffed resumes. Keyword filters get gamed in a single prompt.
Keyword filters miss talent
Self-taught engineers, career-switchers, and OSS contributors without big-name companies get filtered out before a human ever looks.
Senior GitHub is mostly private
The most experienced engineers have the least public code. Star counts and follower numbers tell you almost nothing about depth.
Screening is brutal labor
Recruiters scan 200+ resumes per role to find 5 worth a phone screen. That's not a process — that's a tax on hiring.
// how it works
A hiring engine that reads code, not keywords.
Four stages. One feedback loop. Continuously learning from every hire.
Ingest real work
GitHub repos, PRs, commits, code reviews, resume narrative, career trajectory — all ingested via OAuth or upload.
$ rh ingest --source github,resume --depth 90dExtract signal
Claude-powered pipelines build a Candidate Intelligence Graph: code complexity, ownership, authenticity, domain depth.
→ candidate_signals: 47 dimensions, confidence 0.92Match multidimensionally
Capability, growth, domain, culture — each scored independently. Embeddings + learned re-ranking, never keywords.
match.score = f(capability, growth, domain, culture)Learn from outcomes
Every interview, offer, and 90-day retention rating feeds the loop. Signals that predicted real hires get more weight.
model.retrain ← outcome(hire_quality_90d)47 signals.
One honest score.
Most platforms have one or two signals — years of experience, tech tags — and pretend those are enough. We extract dozens of orthogonal signals from real work, weight them against real hiring outcomes, and never expose the scoring methodology to candidates.
// match_score.breakdown
Code complexity
Per-domain depth scoring from architecture patterns and dependency choices.
Contribution authenticity
Detects fork-and-rename, bulk commits, template scaffolds. Real work only.
Ownership signals
Distinguishes "led / architected / owned" from "helped / assisted" — at scale.
Tech stack depth
Multi-year vector per stack, not a self-reported skill list with 14 logos.
Timeline consistency
Cross-references claimed tech with actual GitHub timeline. Catches inflation.
Career trajectory
Company-tier normalization + role progression rate. No more pedigree blindness.
Collaboration footprint
PR descriptions, review comments, response quality — communication is a signal.
Bias audit trail
Every automated ranking decision is logged. Demographic correlation alerts built in.
Talk to your pipeline like it's a teammate.
Natural-language search. AI-ranked shortlists. Per-candidate summaries written by a model that's read every line of their public code. Screening time drops by 60% — quality goes up.
- Ranked candidate lists per role, refreshed every hour
- AI summary cards: 3-sentence capability, strengths, gaps, interview focus
- NL search: "Backend engineers with PG optimization & startup background"
- Auto-shortlist + one-click "not a fit" — feedback trains the model
- Pipeline funnel + signal-quality analytics built in
Sasha P.
Backend · 4y · ex-seed startup
Daniel K.
Platform · 3y · scale-up
Ari N.
Backend · 2y · YC W24
Lin J.
Backend · 5y · solo founder
Profile strength
+12% since linking GitHub
GitHub connected · 24 repos analyzed
Resume parsed · 3 prior roles
Add 1 more project to unlock 4 new role types
Link LeetCode / Codeforces (optional, +signal)
top picks for you
3 new roles match your distributed systems + PostgreSQL depth. Series A, remote OK.
Your code is your resume.
Spend zero minutes writing bullet points. Connect GitHub, drop in a resume, and we'll do the rest — surfacing roles where your actual depth matches what the team actually needs. No AI-resume generation. No score-gaming. Just signal that's already in your work.
- Opaque profile-strength prompts — gamified, never gameable
- AI-curated weekly top picks with plain-English match reasoning
- Smart apply assist: surfaces which projects to highlight per role
- Built-in scheduling with calendar sync + ICS reminders
- Privacy-first: opt out of any signal, export or delete anytime
60%
Screening time saved
Recruiters spend the hours on real conversations.
3×
Shortlist-to-interview rate
Industry baseline: 10%. Ours: 30%+.
47
Signals per candidate
Code, career, behavioral, outcome — orthogonal by design.
0
Keyword filters
No "5 years of React" gating. Real depth, real comparison.
// faq
Questions every hiring team asks.
How is this different from a traditional ATS?+
ATS systems match resume keywords. Hyrrd reads the underlying work — public repos, commit patterns, PR descriptions, architecture choices — and produces a multi-dimensional match score across capability, growth fit, domain fit, and culture fit.
Can candidates game the match score?+
Match scores are derived from dozens of orthogonal signals — code complexity, ownership verbs, contribution authenticity, timeline consistency — and the methodology is hidden from candidates. No single metric is gameable, and we monitor for manipulation patterns.
What about engineers whose best work is private?+
Senior engineers' public GitHub is often light. We blend GitHub signal with resume depth, impact statements, ownership signals, and company-tier normalization. The model weights whichever signal is more reliable for each candidate.
Do candidates see their score?+
No. Candidates see profile-strength prompts ("add 2 more projects to unlock more matches") but never the raw score. This protects signal authenticity and stops the platform from devolving into score-optimization games.
How do you prevent bias?+
Every automated ranking decision is logged. We monitor demographic correlation with output rankings and alert when specific signal types correlate with protected characteristics. Audit logs are exportable for compliance review.
Is this only for senior engineers?+
No. Match weights adapt by seniority. For freshers, GitHub activity, side projects, and competitive coding profiles dominate. For staff+ engineers, architecture ownership and impact statements dominate. The signal mix is learned, not hardcoded.
Stop reading resumes.
Start reading signal.
Join the private beta. Two-minute setup. First ranked candidates within 10 minutes of posting your first role.