AI Engineer

Recruits Lab exists to place the scientists, builders, and commercial leaders who move life sciences and technology forward. We do it through candidate-centric relationships and data, not volume. This role exists so our recruiters stop guessing and start matching with evidence: you will turn our search process into a living system that finds passive talent faster, scores fit more honestly, and compounds every placement we make.

Performance Objectives

• Ship a production matching engine within 90 days that ranks passive candidates against calibrated role profiles and lifts shortlist-to-interview conversion by 25% on life sciences and tech searches.
• Build and maintain sourcing models that surface named-account and territory-fluent talent (the people who never apply) so recruiters spend time on conversations, not lists.
• Instrument every search with measurable signals—quota history proxies, buyer-type fit, cycle length—so we can defend every shortlist with data, not résumé claims.
• Reduce time-to-calibrated-shortlist on specialized searches from weeks of manual mapping to days without sacrificing relationship quality.
• Partner with recruiters after each placement to retrain models on what actually closed, so the next search in the same territory or buyer set starts smarter.
• Own reliability and privacy of candidate and client data so our process stays defensible with pharma, biotech, and SaaS hiring teams.

Environment & Resources

You report to leadership in a 11–50 person, early-stage firm based in Fort Lauderdale. You sit next to recruiters who already run calibrated, passive-first searches in histology, digital pathology, SaaS, and R&D. You get real search data, client feedback loops, and the mandate to productize what currently lives in people’s heads. Stack and tooling are yours to choose as long as they ship and stay secure.

Essential Qualifications

• Shipped ML or ranking systems used by non-engineers in a high-stakes matching or recommendation context—not demos.
• Proven ability to turn messy, relationship-heavy data (territory, buyer type, deal motion) into features that improve outcomes, not just accuracy scores.
• Comfort building end-to-end: data pipelines, models, simple interfaces recruiters will actually use daily.
• Track record of iterating from production feedback in weeks, not quarters.
• Judgment around PII, bias, and auditability in talent decisions.

Role Selling Points

• Your work lands in live searches that place Area Sales Managers and technical leaders into pharma, biotech, and academic labs—impact you can name.
• Tiny team, no layers: you own the AI surface of a specialized firm that already wins by sourcing people who never apply.
• Life sciences + tech dual market gives you richer signals than generic ATS vendors ever see.
• Early enough that the matching engine you build becomes how Recruits Lab is known.

If you want your models to decide who gets the call that changes a territory, apply. Show us one system you shipped that changed how humans made a hard matching decision.