Research Engineer

You'll build the evaluation systems that tell us whether Firecrawl actually works. That sounds simple. It isn't. Our core promise, convert any URL into clean, structured, LLM-ready data reliably, is hard to measure rigorously across millions of different websites, formats, and edge cases. As the systems we're measuring get more complex, the question "did that work?" gets harder, not easier.

This isn't an eval role where you inherit a framework and run benchmarks. You'll design the metrics, build the pipelines, generate the datasets, and own the feedback loop from output quality back to model and product decisions. If you care about what "good" actually means and have the engineering depth to measure it, this is the role.

Salary Range: $210,000–$275,000/year (Range shown is for U.S.-based employees in San Francisco, CA. Compensation outside the U.S. is adjusted fairly based on your country's cost of living.)

Equity Range: Competitive equity — details shared during the process.

Location: San Francisco, CA (Hybrid, on-site required)

Job Type: Full-Time

Experience: 4+ years in ML, research engineering, or data-heavy backend, with real evaluation work

Visa: Must be legally authorized to work in the United States. We're not able to sponsor visas right now, though that may change down the line.

Firecrawl is the easiest way to turn the web into data AI agents can use. One API call converts any URL into clean, LLM-ready markdown or structured data - the boring-hard problem everyone building with LLMs eventually hits, solved.

We hit 8 figures in ARR in year one and more than doubled it in year two. We have 150k+ GitHub stars, and developers, agents, and category-defining AI companies build on us every day. Growth like this is rare, and we're just getting started.

We're a small team punching far above our weight. Everyone here owns a real piece of the product and company, end to end, and runs it themselves - no hiding behind process or headcount.

This is a place for people who want to work at the frontier: an AI company building the infrastructure other AI companies run on, not one bolting AI onto an existing product. We move fast, go deep, and are building the tools superintelligence will rely on to gather data from the web.

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  Design the metrics that define what "good output" actually means across millions of sites, formats, and edge cases

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  Build the pipelines and harnesses that measure quality rigorously and at scale

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  Generate and curate the datasets that make evaluation trustworthy

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  Own the feedback loop from output quality back to model and product decisions

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  Turn "did that work?" into an answer the whole team can act on

• You have the engineering depth to build real evaluation systems, not just run existing ones

• You care deeply about what "good" means and how to measure it rigorously

• You're comfortable owning ambiguous problems where the metric itself has to be invented

• You move fast and close the loop - you'd rather ship, measure, and iterate than perfect on paper

• Someone who only wants to run benchmarks someone else designed

• A pure researcher who won't build the systems, or a pure engineer who won't think about methodology

• Someone who needs a fully-specced ticket to start

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.

• Salary that makes sense — $210,000-$275,000/year (U.S.-based), based on impact, not tenure

• Own a piece — Gain competitive 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 all parents

• 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

• 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 to help your wallet out a bit

• Pet insurance — Because fur babies are family too

• 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

1. Application Review — Send us your work and a quick note on why this excites you. Show us what you've built — eval systems, metrics you designed, datasets you created, quality problems you measured. We care about what you've shipped, not where you went to school.

2. Intro Chat (~25 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.

3. Technical Chat (~45 min) — We'll dig into a real problem from our world — how you'd measure whether messy web output is actually "good," and build the system to prove it. Come ready to think out loud; we care how you reason, not whether you memorized the answer.

4. Founder Chat (~25 min) — Culture, pace, ownership, and how you like to work. Time for your questions too.

5. Paid Work Trial (1-2 weeks) — Work with the team on a real, scoped evaluation problem — paid at a contractor rate. It's the truest signal for both sides: you see what building at Firecrawl actually feels like, and we see how you ship. Remote-friendly, and we'll flex around your current commitments.

6. Decision — We move fast after the trial.

If you care about what "good" actually means and have the engineering depth to measure it, you should join us.

👉 Apply now.