AI Engineer

Recruits Lab exists to place the right life sciences and technology talent into companies that cannot afford a miss. You will turn that mission into working systems: models, pipelines, and tools that find passive specialists, score fit against culture and quota, and shorten searches that used to take a quarter. This role matters because our edge is not more calls—it is better signal, faster, at the moment a scientist or sales leader is ready to move.

Performance Objectives

• Ship a production matching model within 90 days that ranks passive candidates against calibrated role profiles and lifts shortlist quality versus current recruiter scoring.
• Automate sourcing and enrichment so recruiters receive named, verified talent maps for life sciences and SaaS searches instead of raw lists.
• Cut time-to-calibrated-shortlist on priority searches by embedding ranking, duplicate detection, and relationship-signal features into daily workflow.
• Instrument search outcomes (placement, ramp, client feedback) so every model iteration is judged on hire quality, not vanity metrics.
• Partner with recruiters to productize one high-leverage workflow per quarter (territory mapping, technical-screen assist, or forecast hygiene) that they actually use.
• Keep data, prompts, and models auditable so client-facing processes remain defensible in regulated life sciences hiring.

Environment & Resources

You report to leadership in a 11–50 person, early-stage Fort Lauderdale firm that already sells AI/ML fluency as a specialty. You sit with recruiters who live in pharma, biotech, digital pathology, and SaaS searches—not a separate lab. Expect pragmatic stack choices, production ownership, and direct access to search data and client feedback. Budget and tooling follow proven impact, not a fixed R&D theater.

Essential Qualifications

• Shipped ML or LLM systems that changed a real workflow—ranking, extraction, matching, or recommendation—not only notebooks.
• Strong Python and data-pipeline skills; you can go from messy CRM/ATS text to a reliable feature set and evaluation harness.
• Experience evaluating models on business outcomes (precision at shortlist, recruiter adoption, time saved), not leaderboard scores alone.
• Comfort working with unstructured professional data, entity resolution, and retrieval or ranking under noisy labels.
• Ability to partner with non-engineers: translate a search strategy into a measurable product and ship the first version fast.

Role Selling Points

• Your models sit on live, high-stakes searches in life sciences and tech—not toy datasets.
• Small team: you own the AI product surface, from prototype to recruiter adoption.
• Domain density: pathology, biotech R&D, SaaS, and executive search data that generalist AI roles never see.
• Candidate-centric brand: systems that respect people and relationships, not spray-and-pray automation.

If you want to build the intelligence layer that wins the searches others cannot source, apply now. Show us one system you shipped that changed how experts actually work.