A fast-scaling, venture-backed startup is building the AI layer for one of the largest, least-optimized markets: hiring. The platform connects top companies with a distributed network of specialized recruiters and is already generating millions in revenue with a small, highly technical team.

You’ll be working on a system with high-volume, high-signal data—candidate profiles, job requirements, recruiter behavior, and hiring outcomes, creating a rare opportunity to build AI systems with tight feedback loops and clear business impact.

This is not an “LLM wrapper” role. The core problems require retrieval, ranking, orchestration, and systems design under real-world constraints.

What You’ll Do

1. Build and ship production-grade LLM systems for matching, ranking, and search across a dynamic, two-sided marketplace
2. Design RAG pipelines over large, continuously updating datasets with strict latency and relevance constraints
3. Develop agentic workflows that automate multi-step processes (candidate discovery → evaluation → iteration loops)
4. Combine LLMs with classical ML (ranking, recommendation, classification) to hit real-world targets on cost, latency, and quality
5. Own systems end-to-end: data → modeling → infra → deployment → monitoring → iteration
6. Design evaluation loops tied to business metrics (placement success, response rates, time-to-fill)
7. Work closely with product to turn model capabilities into high-leverage features that move revenue

What We’re Looking For

1. 2–5 years building and shipping real-world ML/AI systems in production
2. Experience working on LLM systems beyond prototypes (RAG, agents, tool use, orchestration)
3. Strong intuition for retrieval + ranking systems and working with messy, evolving datasets
4. Ability to make pragmatic tradeoffs between model quality, cost, and latency
5. Comfortable operating in high-ownership, low-process environments (you’ve likely been at a Series A/B company)
6. You care about shipping fast, measuring impact, and iterating from real usage

Nice to have:

1. Experience with search, recommendation, or marketplace systems
2. Familiarity with evaluation, observability, and online experimentation for AI systems
3. Background working on data-rich products with tight feedback loops

Why This Role

1. Early-stage leverage: small team, meaningful ownership over core AI systems
2. Revenue impact: systems you build directly influence millions of dollars in marketplace activity
3. Real data, real feedback loops: not synthetic benchmarks—live production signals
4. Hard problems: retrieval, ranking, and agent orchestration at scale
5. Fast iteration cycles: ship → measure → improve, without heavy process

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