Product Data & AI Evaluation Lead at Staffer.ai

Talent teams are stuck in tools built a decade ago. We're replacing them.

Staffer is an AI-native hiring platform that actually works the way recruiters think: it learns your company, writes roles, searches 850M+ profiles, scores candidates, and runs personalized outreach - all while keeping candidates in the loop through a transparent portal. The response from teams escaping legacy ATS hell has been overwhelming. This isn’t incremental improvement; it’s a complete rethink of how hiring should work.

You will design the systems that define quality.

Shape the metrics behind sourcing intelligence and the evaluation engine behind our LLM agents, turning subjective feedback into measurable truth and driving continuous improvement across the platform.

If owning the architecture that makes AI hiring better week after week sounds meaningful, this is it.

What You’ll Do

Design and Own Sourcing Metrics
You will turn subjective product feedback into structured, quantitative signals that drive improvement.

• Partner with product and engineering to define sourcing quality metrics (relevance, match accuracy, diversity)

• Build and validate measurement frameworks from scratch

• Translate qualitative feedback into SQL-based metrics

• Communicate metrics and analytic logic across teams

• Own the feedback → metric → business insight loop

Build LLM Evaluation Systems
We run multiple LLM-driven agents and need a systematic way to track quality and improvement.

• Define evaluation matrices and success criteria (hallucination rates, tool accuracy, consistency)

• Implement evaluation frameworks using Langfuse (or similar tools)

• Build monitoring, baselines, and continuous improvement processes

Contribute Technically

• Write production Clojure / ClojureScript where needed

• Collaborate with senior full-stack engineers

• Maintain high code standards and quality

What We’re Looking For

• Expert-level PostgreSQL and SQL optimization skills

• Experience designing end-to-end metrics frameworks from ambiguous requirements

• Strong data modeling skills

• Experience with LLM systems in production and evaluation methodologies

• Familiarity with Langfuse or similar LLM operations tools preferred

• Excellent communicator with the ability to translate technical logic to product and engineering stakeholders

Nice to Have

• Experience with semantic search (embeddings, vector databases)

• Machine learning experience

• Experience in recruiting or HR-tech domain

What Success Looks Like

• Month 1: Defined and implemented sourcing metrics framework

• Month 2: Product team using structured metrics; LLM evaluation baseline established

• Month 3: Clear improvements measured in sourcing quality and agent performance

Who You Are

• You are a systems thinker who understands how to go from ambiguity to a working measurement system

• You are a translator between product intuition and engineering precision

• You own problems end-to-end

• You ship pragmatic working solutions and iterate based on data

Who You Are Not

• A pure BI analyst

• A research-only ML scientist

• A prompt tinkerer without systematic evaluation experience

• A performance-only engineering specialist

Why Staffer?

Staffer.ai is part of the Vergence Group - a remote-first, product-centric engineering org with a strong engineering culture and Clojure DNA. You’ll join a small, senior team where your work has direct impact on product outcomes.

What We Offer

- Flexible, remote-first culture

- Competitive annual salary based on experience, plus stock options

- All the gear and tools you need to thrive covered

Staffer is where innovation meets execution. If you’re ready to work on meaningful AI-powered systems and push the boundaries of what’s possible in intelligent hiring, we’d love to hear from you.

Apply now and be part of a team shaping the future of hiring.