Search Engineer, AI Infrastructure

📋 Job Overview

Lavendo partners with startups and high‑growth companies to help them hire top‑tier sales, GTM, and technical talent. This role is with one of our clients; we’ll share full details about the company and interview process as we get to know you and confirm mutual fit.

🏢 About Lavendo

Our client builds the data layer that AI agents run on. One API call turns any URL into clean, structured, LLM-ready data. It is already the default way a huge number of developers pull web data into their AI systems.

The numbers back it up: 8 figures in ARR in year one, more than doubled that in year two, and 180,000+ GitHub stars, placing its open-source project among the top 50 repositories of all time. They just closed a $75M Series B. The company is small, moving fast, and hiring people who want to build the next chapter with them.

🎯 The Role

As a Search Engineer, you will own ranking quality and relevance for an LLM-driven search and retrieval product.

This is a role for an engineer whose primary, hands-on discipline is production search quality. You have personally shipped ranking or re-ranking changes, measured their impact, and can clearly describe the relevance, recall, conversion, or other search-quality metric you improved—including the result.

You will work directly with the Head of Search and a small team across crawling, indexing, retrieval, and search serving. These systems are central to delivering excellent results at scale, but they are not substitutes for relevance ownership. Your role is to improve search quality while navigating the real constraints of web-scale retrieval: freshness, deduplication, latency, reliability, and cost per query.

✅ Key Responsibilities

• Own ranking quality and relevance for LLM-driven retrieval
• Design and build the crawling, indexing, and retrieval systems
• Push down latency and cost per query while search volume grows
• Own services end to end: design, ship, monitor, and iterate in production
• Work directly with the Head of Search and the rest of the search team on the roadmap

📌 Required Qualifications

• 3+ years of experience building and operating search or retrieval systems in production, with ranking and relevance as your primary, owned discipline
• A specific example of a ranking or re-ranking change you personally owned, the search-quality or business metric it moved, and the numerical impact achieved
• Hands-on information-retrieval depth across BM25 and lexical retrieval, semantic/vector retrieval, hybrid retrieval, re-ranking, retrieval evaluation, or RAG evaluation
• Experience measuring search quality through relevance, recall, conversion, CTR, NDCG, MRR, query satisfaction, or comparable production metrics
• End-to-end ownership of search services: you have designed, shipped, monitored, debugged, measured, and iterated on production systems
• Strong backend systems programming skills in Go, Rust, Python, or a comparable language.
• Experience balancing relevance, latency, cost, correctness, and reliability under real production constraints
• Comfort working through ambiguity, setting technical direction, and shipping quickly with a build-measure-iterate operating style

🎁 Benefits

• San Francisco: $235,000–$260,000 USD base salary, plus competitive equity.
• Toronto: $217,000–$233,000 CAD base salary, plus competitive equity.
• 100% employer-paid medical, dental, and vision (50% for spouse/kids); employer-paid life, short & long-term disability; 401(k); pre-tax FSA and commuter benefits; pet insurance; 15+ days PTO; 12 weeks fully paid parental leave; $100/month wellness stipend; $1,000/year L&D stipend; 3-month paid sabbatical after 4 years; team offsites twice a year; SF HQ perks; loaner e-bike

🛂 Visa & Eligibility

Not open to visa sponsorship on this one (Candidates must be authorized to work in the United States or Canada).