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Paraform is an end-to-end AI recruiting marketplace connecting companies with specialized recruiters and AI agents. The company has 20 engineers, over $100M ARR, and is scaling the engineering team 3x over the next year.
You will lead the AI Platform engineering team, which owns the matching, ranking, and agentic systems that determine which recruiter sees which role, which candidate reaches which company, and how much of the hiring process runs autonomously. This is a hands-on technical leadership role where you'll manage a pod of Applied AI, ML, and Product engineers while staying deeply involved in core ML work.
You'll spend at least one-third of your time building: reading PRs, reviewing model evaluations, and shipping production code every week. You're accountable for outcomes—match quality, automation rate, and hiring velocity—not just output.
Key responsibilities include:
- Hiring, coaching, and leveling a small autonomous team of applied AI, ML, and Product engineers
- Owning core ML systems at the code level: data strategy, feature and embedding design, model selection, training, and fine-tuning
- Tackling the hardest modeling problems: ranking plateaus, offline/production eval disagreements, cold-start challenges
- Building and improving matching and ranking models that directly impact revenue
- Developing LLM-powered agentic workflows that automate work for recruiters, hiring managers, and ops teams
- Implementing trust and safety systems: fraud detection, spam filtering, and guardrails for AI fairness and accuracy
- Establishing rigorous evaluation frameworks that measure quality, reliability, and business impact in production
- Managing the quality/cost/latency tradeoffs as the system scales
- Translating ambiguous business goals into quarterly plans and shipping them
Paraform is backed by Scale, Felicis, A*, BOND, Liquid 2, DST Global, and founders from YouTube, Instacart, Canva, and others. Customers include Decagon, Palantir, Rippling, Abridge, and Hightouch. The company grew revenue 8x last year.
REQUIREMENTS:
- Staff-level IC depth in ML or applied AI: you're the person others pull in for genuinely hard problems and have stayed technical while leading
- 1+ years managing or formally tech-leading engineers (Tech Lead Manager role) OR 4+ years setting technical direction across multiple teams (Technical Director path)
- Shipped production ML or LLM systems used by real people at scale: retrieval, ranking, recommendation, agentic workflows, or similar
- Eval-driven: you measure model quality against business outcomes, not just offline benchmarks, and can distinguish real wins from noise
- Data-fluent: can write SQL, sanity-check experiment results, and push back on bad metrics
- AI fluency is a floor, not a differentiator; you use AI tools in your own work and know how to increase team leverage with them
- Strong written communication: you document operating norms, decision docs, and postmortems to keep small teams fast as they scale
- You still want to build: writing production code every week is core to the role, not a step backward
