Firecrawl is the web data API to search, scrape, and interact with the web at scale. Turn any source into clean Markdown or structured data your agents can ship with. Backed by Y Combinator.
About the role
Design and ship A/B tests across agent-facing surfaces (MCPs, CLIs, skills, APIs, documentation) to see how agents actually use Firecrawl Turn experiment results into shipped product improvements, fast
What they're looking for
- Salary Range: $235,000–$260,000 USD/year (SF) / $217,000–$233,000 CAD/year (Toronto)
- Equity Range: Competitive equity. Details shared during the process
- Location: San Francisco, CA (SF HQ) or Toronto, ON (Toronto Hub). On-site, five days a week
- Job Type: Full-Time
- Experience: 5+ years
- Work Authorization: Must be authorized to work in the United States or Canada. We're not able to sponsor US visas right now. For Canada, we'll consider sponsorship on a case-by-case basis through our Toronto Hub
More about this role
Firecrawl is looking for a high-agency, product-minded engineer with strong experimental and data instincts to own how agents discover, understand, and use Firecrawl. You'll ship fast, run rigorous A/B tests, and turn what you learn into a better product, for an audience that isn't human.
This is not a pure research or data science role. We want an engineer who brings scientific rigor to product development: forming hypotheses, running experiments, analyzing results, and shipping better agent experiences. Prior agent experience is valuable, but raw engineering ability, shipping velocity, and learning speed matter most.
Equity Range: Competitive equity. Details shared during the process.
Job Type: Full-Time
Experience: 5+ years
Work Authorization: Must be authorized to work in the United States or Canada. We're not able to sponsor US visas right now. For Canada, we'll consider sponsorship on a case-by-case basis through our Toronto Hub.
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. It's the boring-hard problem everyone building with LLMs eventually hits, solved.
In September...
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