OFIA · CASE STUDY
Research Talent Scout
AI agent that monitors arXiv and GitHub trending for matching research, identifies authors, scores fit, and creates Ashby candidates with personalized outreach.
The problem
The best engineering hires — especially in AI and machine learning — aren't on job boards. They're publishing papers on arXiv, contributing to open source repos, and building in public on GitHub. Traditional recruiting had zero visibility into these channels. By the time a strong researcher submitted a resume, they'd already received multiple offers. The recruiting team was fishing in the wrong pond.
Our approach
An agent that continuously monitors arXiv, Google Scholar, and GitHub trending for research and engineering work aligned with the company's technical focus. When it finds a match, it identifies the author, cross-references on LinkedIn for background and availability, scores fit, and pushes a fully documented candidate into the ATS — complete with a personalized outreach note referencing their specific work.
How it works
- Watches configured research topics on arXiv and GitHub trending repositories.
- Scores each paper or repo for topic relevance.
- Extracts author information and looks up LinkedIn for role, experience, and publication history.
- Computes a fit score combining technical alignment, seniority signals, and likely availability.
- Creates a candidate record in Ashby via MCP with source link, score, key publications, and a personalized outreach note.
- Notifies recruiting in Slack with full context and an Ashby record link.
What we shipped
- arXiv + GitHub trending monitor
- Topic-relevance scorer
- LinkedIn cross-reference + fit score
- Ashby candidate creation via MCP with personalized outreach
- Slack notification with full provenance
Impact
- 12 research-caliber candidates sourced entirely outside traditional pipelines.
- 4 hired.
- Time from paper publication to recruiter outreach: 48 hours.
Frequently asked questions
How can companies recruit AI researchers who are not on job boards?
Companies can recruit AI researchers not on job boards by deploying an agent that monitors arXiv new submissions and GitHub trending, identifies relevant authors, and creates ATS candidates with personalized outreach — surfacing talent within 48 hours of publication.
Can AI automate technical recruiting for machine learning roles?
Yes. AI can automate technical recruiting for ML roles by continuously monitoring research outputs and open source contributions, scoring authors against a technical focus area, and generating ATS records with personalized outreach notes.
What is the typical time from paper publication to recruiter outreach with an AI scout?
With an AI talent scout agent, the time from paper publication to a recruiter outreach draft in the ATS is approximately 48 hours — compared to weeks or never with manual monitoring.
WANT THE SAME
Build this for your team in 4 weeks
Ofia is the AI build partner for mid-market knowledge orgs. We map your operating norms, ship the first agent in 2–4 weeks, and hand you the platform that runs them.
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