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Complete transparency on every dimension, weight, confidence band, and bias check. If you can see inside the model, you can trust the output.
Why we publish this: HireSignal is not an AI wrapper. The core scoring model is deterministic — same GitHub data always produces the same score. LLaMA 3.1 enriches the report with narrative and interview questions, but it doesn't set the number. Publishing the model is evidence of that. Wrappers don't have documented, explainable models.
Total maximum score: 145 points (normalised to 100 via the weighted formula above).
Max 10 points
Signals
Confidence
Very high — all fields are directly observable from the GitHub API response.
Max 25 points
Signals
Confidence
High — repo metadata is public; occasional private repos may be missed.
Max 10 points
Signals
Confidence
Moderate — stars are visible but can be gamed via star-farming. Flagged when detected.
Max 20 points
Signals
Confidence
Moderate — GitHub Events API only returns the last 300 events / 90 days. Long-tenure corporate engineers with private activity will score lower here.
Max 15 points
Signals
Confidence
High — language data is per-repo and reliably reported by GitHub.
Max 5 points
Signals
Confidence
Low — follower counts are lagging signals and easily inflated. This dimension has the lowest weight in all role presets.
Max 20 points
Signals
Confidence
High when ≥30 commits sampled. Low for accounts with < 10 commits visible.
Max 10 points
Signals
Confidence
High — timestamps are reliable. Note: engineers between jobs will naturally score lower here.
Max 10 points
Signals
Confidence
Low to moderate — PR Review events are under-represented in the public Events API. Strong corporate contributors will have invisible review activity.
Max 10 points
Signals
Confidence
Moderate — public OSS events are visible; corporate open source (behind VPNs or enterprise GitHub) is invisible.
Max 10 points
Signals
Confidence
Moderate — README presence is inferred; full content is not fetched to stay within API rate limits on the free tier.
Every report includes an overall data confidence score (30–98%) and a per-dimension confidence band (high / moderate / low). These are shown as coloured pills on each scoring bar so recruiters never mistake a low-data estimate for a high-confidence fact.
High confidence
Threshold: ≥ 80
1,247 commits · 4 years of activity
Moderate confidence
Threshold: 50–79
Some data but limited events
Low confidence
Threshold: < 50
Only 3 repos, mostly forks
The hardest case: Senior engineers at BigCo. Staff engineers at Google, Meta, or any company with private GitHub Enterprise will show minimal public activity — they're working in private repos all day. When confidence is low and the inferred experience level is Senior/Staff, HireSignal automatically upgrades a NO_HIRE to MAYBE and shows a data completeness warning.
Role presets apply weight multipliers to dimensions without changing the raw scores. The final 0–100 score reflects relative emphasis — a backend engineer is judged primarily on commit quality, not social proof.
Backend Engineer
Emphasises commit quality, contribution consistency, and technical breadth.
Frontend / Full-Stack
Balances breadth with repo quality and documentation — UI engineers often have polished public work.
ML / AI Engineer
Heavy weight on commit quality and recency — ML work is often in Jupyter Notebooks; we account for that.
DevOps / Platform
Values consistency and PR review quality — infra engineers often review more than they push.
OSS Contributor
Maximises community impact and OSS contribution signals.
Balanced (Default)
Equal-weight baseline. Recommended for general screening.
Enterprise customers can define custom weight multipliers per role. Pro customers can override the preset on any individual analysis.
HireSignal is designed for NYC Local Law 144 compliance and EU AI Act Article 22. The following automated checks run on every analysis and are recorded in the audit log.
| Flag | Trigger condition | Mitigation action |
|---|---|---|
low-data-bias | < 5 original repos OR hireConfidence < 50% | Upgrade NO_HIRE → MAYBE for Senior/Staff engineers. Show data completeness warning banner. |
recency-bias | Account inactive in the last 6 months | Surface warning: candidate may be employed, on leave, or working in private repos. |
popularity-bias | Community Impact score is the highest-weighted dimension AND total score > 80 | Warn: high star counts may reflect trending projects rather than engineering skill. |
ai-inflation | AI usage likelihood ≥ 40% (detected via commit pattern analysis) | Surface AI usage flag with tailored interview probe questions. Score is not adjusted — human judgment required. |
LLaMA 3.1 8B runs self-hosted on HireSignal infrastructure. It is used for three things:
Zero data leaves your trust boundary. No candidate data is sent to OpenAI, Anthropic, Google, or any third-party LLM API. The LLaMA model runs in HireSignal's private inference cluster. Enterprise customers can optionally deploy the model on their own infrastructure.
When a recruiter marks a candidate as hired, HireSignal sends a 90-day and 180-day check-in asking for a performance rating (output quality, team fit, retention risk, would hire again). These ratings are stored against the original score.
After 50+ outcomes, the dashboard shows which HireSignal score bands correlate with high performers for that specific recruiter's hiring patterns. After 100+ outcomes, Enterprise customers can request auto-suggested weight recalibrations.
This dataset is the product's primary moat. No base model + prompt can replicate recruiter-specific outcome data. After 10,000 hires across users, the correlation dataset becomes a proprietary asset that defines HireSignal's accuracy advantage.
Questions about the model? We're happy to walk through any dimension in detail.