You'll own the search and information retrieval systems at the core of Firecrawl — the infrastructure that determines how we find, rank, index, and serve web content at scale. Retrieval quality is Firecrawl's deepest moat. As AI agents increasingly depend on multi-step search and enrichment, the gap between good retrieval and great retrieval compounds. You're the person who closes that gap — and widens it against every competitor. This is a full-stack search role where you'll build and operate everything from ingestion pipelines to serving layers. If you've built search indexes at massive scale and care deeply about ranking quality, freshness, and retrieval speed, this is the role.
Salary Range: $180,000–$290,000/year (Range shown is for U.S.-based employees. Compensation outside the U.S. is adjusted fairly based on your country's cost of living. You can explore how we calculate this here: https://www.firecrawl.dev/careers/compensation.)
Equity Range: Up to 0.15%
Location: San Francisco, CA or Remote (Americas, UTC-3 to UTC-10)
Job Type: Full-Time
Experience: 3+ years building search/IR systems at scale
Visa: US Citizenship/Visa required for SF; N/A for Remote
About Firecrawl
Firecrawl is the easiest way to extract data from the web. Developers use us to reliably convert URLs into LLM-ready markdown or structured data with a single API call. In just a year, we've hit 8 figures in ARR and 100k+ GitHub stars by building the fastest way for developers to get LLM-ready data.
We're a small, fast-moving, technical team building essential infrastructure superintelligence will use to gather data on the web. We ship fast and deep.
What You'll Do
Build and operate search indexes at massive scale. Design, build, and maintain the indexing infrastructure that powers Firecrawl's core product. You'll handle billions of documents and care about every millisecond of latency and every byte of storage.
Own the full stack from ingestion to serving. You don't just build one piece — you own the entire pipeline. Ingestion, processing, indexing, ranking, query understanding, and serving. When something breaks at 3am, you know where to look because you built it.
Solve ranking, relevance, and query understanding. Make sure the right content surfaces for the right queries. You'll build and iterate on ranking models, relevance scoring, and query parsing systems that directly impact product quality.
Tackle freshness, dedup, and incremental indexing. The web changes constantly. You'll build systems that keep our index fresh without re-crawling everything, deduplicate content intelligently, and handle incremental updates at scale without rebuilding from scratch.
Run experiments and ship results to production. You design experiments, measure results rigorously, and ship winners to production fast. You don't need someone to tell you what to try next — you have a backlog of ideas and the judgment to prioritize them.
Collaborate closely with the team. Work directly with the RL-focused Research Engineer and the engineering team to connect search/IR improvements with model training and the broader product roadmap.
What We're Looking For
Has built search indexes at massive scale. Not a tutorial project — real indexes serving real traffic with real latency requirements. You've dealt with the hard problems: sharding strategies, index compaction, schema evolution, and the operational complexity of keeping billions of documents queryable and fast.
Hands-on with ranking, relevance, and query understanding. You've built or meaningfully improved ranking systems. You understand BM25, learned ranking, embedding-based retrieval, and when to use which. You can reason about relevance tradeoffs and you've shipped ranking changes that moved metrics in production.
Owns the full stack: ingestion → index → serving. You're not a specialist who only touches one layer. You've built and operated the entire search pipeline — from how documents enter the system to how results get served. You understand the dependencies between layers and make good architectural decisions because you see the whole picture.
Has solved freshness, dedup, and incremental indexing problems. You know that building the initial index is the easy part. Keeping it accurate, fresh, and deduplicated at scale is where the real engineering lives. You've built systems that handle continuous updates without full rebuilds and you've debugged the subtle correctness issues that come with incremental processing.
Self-directed experimenter who ships without handholding. You generate your own hypotheses, design your own experiments, and ship your own code. You don't wait for a roadmap or a sprint planning meeting. You see what needs to improve, you try something, you measure it, and you ship it if it works.
Backgrounds that tend to do well: Search engineers at companies with large-scale indexes — web search, e-commerce, document search. IR researchers who've shipped their work to production. Infrastructure engineers who've built and operated real-time indexing pipelines. Engineers from Elasticsearch, Algolia, Vespa, or similar search infrastructure teams who got frustrated that they could only tune the knobs and wanted to build the engine.
What We're NOT Looking For
Search users, not search builders. If your experience is configuring Elasticsearch or tuning Solr queries but you haven't built search infrastructure from scratch, this isn't the right role. We need someone who builds the engine.
Researchers who don't ship. If your best search/IR work lives in a paper and you've never deployed a ranking model to production, this isn't it. Every experiment here ends with code running in prod.
Engineers who only work on one layer. If you only do indexing, or only do ranking, or only do serving — and you're not interested in owning the full stack — you'll be frustrated here. We need someone who sees the whole pipeline and can work anywhere in it.
People who need clean infrastructure to be productive. The systems you'll work on are evolving fast. If you need everything to be perfectly abstracted and well-documented before you can contribute, you'll stall. We need someone who can build and improve infrastructure while shipping on it.
A Note On Pace
We operate at an absurd level of urgency because the window for what we're building won't stay open forever. If that excites you, keep reading. If it doesn't, no hard feelings — but this role probably isn't for you.
Benefits & Perks
Available to all employees
Salary that makes sense — $180,000–$290,000/year, based on impact, not tenure
Own a piece — Up to 0.15% equity in what you're helping build
Generous PTO — 15 days mandatory, anything after 24 days, just ask (holidays excluded); take the time you need to recharge
Parental leave — 12 weeks fully paid, for moms and dads
Wellness stipend — $100/month for the gym, therapy, massages, or whatever keeps you human
Learning & Development — Expense up to $1,000/year toward anything that helps you grow professionally
Team offsites — A change of scenery, minus the trust falls
Sabbatical — 3 paid months off after 4 years, do something fun and new
Available to US-based full-time employees
Full coverage, no red tape — Medical, dental, and vision (100% for employees, 50% for spouse/kids) — no weird loopholes, just care that works
Life & Disability insurance — Employer-paid short-term disability, long-term disability, and life insurance — coverage for life's curveballs
Supplemental options — Optional accident, critical illness, hospital indemnity, and voluntary life insurance for extra peace of mind
Doctegrity telehealth — Talk to a doctor from your couch
401(k) plan — Retirement might be a ways off, but future-you will thank you
Pre-tax benefits — Access to FSAs and commuter benefits (US-only) to help your wallet out a bit
Pet insurance — Because fur babies are family too
Available to SF-based employees
SF HQ perks — Snacks, drinks, team lunches, intense ping pong, and peak startup energy
E-Bike transportation — A loaner electric bike to get you around the city, on us
Interview Process
Application Review — Send us your work and a quick note on why this excites you. Show us what you've built — search systems, indexing pipelines, ranking improvements. We care about what you've shipped, not where you went to school.
Intro Chat (~20 min) — A quick conversation to get to know each other before we go deep. We'll talk about what you've been working on, what drew you to Firecrawl, and what you're looking for in your next role. Time for your questions too.
Technical Deep Dive (~60 min) — Go deep on search/IR systems you've built: architecture decisions, scale challenges, ranking approaches, and production tradeoffs. We'll explore a live problem — how you'd approach a real search/indexing challenge at Firecrawl's scale. We're looking for depth across the full stack, production instincts, and the ability to reason about tradeoffs under constraints.
Founder Chat (~30 min) — Culture, pace, ownership, and how you like to work. Time for your questions too.
Paid Work Trial (1–2 weeks) — Tackle a real search/IR problem with production implications. We evaluate on technical depth, experimentation rigor, and how fast you ship something meaningful.
Decision — We move fast after the trial.
If you've built search systems at scale and want to work on one of the most interesting web data problems in AI infrastructure — this is your shot.
👉 Apply now.
About Firecrawl
Firecrawl is a web data infrastructure platform that converts websites into clean, structured data optimized for AI applications through a simple API. Originally built as an internal tool at Mendable to solve their own data ingestion challenges, the team recognized the broader need for reliable web data extraction and transformed it into a standalone product. The platform combines web search, scraping, and AI-powered parsing into a unified solution, focusing on the core developer priorities of reliability, speed, and ease of use. By abstracting away the complexities of web data extraction, Firecrawl serves as the foundational data layer that enables AI applications to seamlessly access and process web content in the exact format they need.
