AI Engineer

Recruits Lab exists to put the right life sciences and technology talent in the right seats—fast, with relationships that last. You will build the AI that turns messy market signal into calibrated shortlists, so our recruiters spend time with people, not databases. This role matters because our clients win when we source passive talent others miss; your models are how we do that at scale.

Performance Objectives

• Ship production matching and ranking models that lift qualified shortlist conversion by 25% within two quarters.
• Automate sourcing signals from public and proprietary data so recruiters receive ranked passive candidates for every live search within 48 hours of kickoff.
• Reduce time-to-calibrated-shortlist on life sciences and SaaS searches from weeks to days without dropping placement quality.
• Stand up evaluation pipelines (precision/recall, human-in-the-loop review) so every model change is measured against real placement outcomes.
• Partner with recruiters to encode “named-account” and territory fluency into features—not generic keywords—so screening matches how we actually hire.
• Document, monitor, and iterate models so they stay accurate as markets, titles, and compensation shift.

Environment & Resources

You report to leadership in a 11–50 person, early-stage Fort Lauderdale firm with a national search footprint. You work directly with recruiters who live in life sciences, biotech, and tech staffing. You own tooling choices (Python, modern ML stack, APIs, LLMs) and have latitude to buy or build what actually moves placement metrics. Budget follows impact, not ceremony.

Essential Qualifications

• Shipped ML or LLM systems that improved a real business metric—not notebooks or demos.
• Hands-on experience with ranking, retrieval, or entity resolution on messy people/company data.
• Ability to translate recruiter judgment (quota history, buyer relationships, territory fit) into features and evals.
• Comfort owning the full loop: data, training, production, monitoring, and iteration with non-engineers.
• Clear communication: you can explain model tradeoffs to a recruiter in one conversation.

Role Selling Points

• Your work lands in live searches for pharma, biotech, digital pathology, and SaaS—not a sandbox.
• Small team: you set the AI roadmap instead of joining someone else’s.
• Domain that rewards precision: life sciences talent is scarce, passive, and relationship-driven—generic AI fails here.
• Direct line from model to placement and client outcome.

If you want to build AI that actually fills hard roles, apply. Tell us a system you shipped and the metric it moved.