Web Automation Engineer

Web Automation Engineer

You'll keep Firecrawl connected to the open web. Every product we ship depends on reliably accessing pages at web scale, and the modern web actively resists automation with detection systems, challenge flows, and fingerprinting that evolve weekly. This role is equal parts research and engineering: understand how those systems work at the deepest level, then turn that understanding into infrastructure that keeps our scraping fast, reliable, and resilient.

If you like taking apart obfuscated JavaScript on a Tuesday and shipping the fix to production on Wednesday, this is for you.

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: 3+ years building browser automation, web infrastructure, or systems-level software

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.

We hit 8 figures in ARR in year one and more than doubled it in year two. We have 180k+ Git Hub stars, putting us in the top 50 repositories of all time, 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, working out of SF HQ and our new Toronto Hub. 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.

• Reverse engineer detection:
  Research how modern websites detect and challenge automated traffic, and take apart those mechanisms and challenge flows

• Find the signals:
  Identify the browser, device, network, and behavioral signals that separate automated access from human traffic

• Replicate and harden:
  Reproduce those signals synthetically in authorized test environments to validate and harden our approaches

• Turn research into production:
  Build the automation, diagnostic, and monitoring tools that keep scrape reliability high as the web shifts

• Own scrape success rate:
  Measure it, explain regressions, and ship the fixes

• Ship fleet-wide:
  Work with platform engineers to roll improvements out across the crawling fleet at scale

• Languages:
  
  Strong JavaScript/Type Script and/or Python, and comfortable picking up whatever a problem demands

• Browser internals:
  Real understanding of rendering, JS engines, headless automation (CDP, Playwright, Puppeteer), and where automated browsers differ from real ones

• Network fundamentals:
  Solid grasp of HTTP/TLS, proxies, and browser and device fingerprinting

• Reverse-engineering instincts:
  You can read minified, obfuscated code and figure out what it’s actually checking

• Research with a shipping habit:
  You go deep on how something works, then land the fix in production, not just a writeup

• Nice to have:
  Deobfuscating JavaScript and VM-based obfuscation, network-level fingerprinting (TLS/HTTP2 signatures, JA3/JA4), running large browser or proxy…