Search Engineer, AI Infrastructure (SF/Toronto)

• 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

• 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

They believe developers and AI agents should not have to fight the web to get useful data out of it. Every hour spent maintaining a scraper, dealing with stale pages, or working around brittle retrieval systems is an hour taken away from the actual product.

Their job is to make that problem disappear: give AI systems reliable access to the live web, then make the results clean, current, structured, and useful enough to power real products.

The people who do well here have personally owned search quality in production, think end-to-end about relevance, crawling, indexing, serving, latency, and cost, and thrive in ambiguity by shipping, measuring, and iterating quickly without needing a fully defined playbook.

WHY JOIN?

• Compensation & Equity:

San Francisco: $235,000–$260,000 USD base salary, plus competitive equity.

Toronto: $217,000–$233,000 CAD base salary, plus competitive equity.

• Traction: 8 figures in ARR in year one, more than doubled in year two. 180,000+ GitHub stars and a developer base showing up before you even start selling to them. $75M Series B closed in 2026, backed by top-tier investors
• Team: ~40 people total, real visibility with the founders and leadership. No layers, no committees, fast decisions, short feedback loops
• Benefits: 100% employer-paid medical, dental, and vision (50% for spouse and kids); employer-paid life, short-term, and long-term disability insurance; 401(k), pre-tax FSA, and commuter benefits; pet insurance; 15+ days PTO; 12 weeks fully paid parental leave for moms and dads; $100/month wellness stipend; $1,000/year learning and development budget; 3-month paid sabbatical after 4 years; team offsites twice a year; SF HQ perks and a loaner e-bike for commuting
• Visa: not open to visa sponsorship on this one (Candidates must be authorized to work in the United States or Canada).

• Intro Chat
• Technical Chat
• Founders Chat
• Paid Work Trial (~40 hours)

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.