Firecrawl
Product Engineer
About this role
Build and ship developer-facing product features at Firecrawl, a rapidly growing AI infrastructure company that converts web data into LLM-ready formats. You'll own features from conception through production, working directly with the engineering team on a small, high-velocity team that prioritizes shipping fast and iterating based on real developer feedback.
What you'll do
- Design and ship developer-facing product features end-to-end from idea to production
- Ensure reliability and quality of features against a constantly changing web landscape
- Transform complex, ambiguous problems into clean, intuitive APIs developers love
- Collaborate with engineering team on roadmap decisions and technical tradeoffs
- Engage with GitHub issues and developer feedback to drive product priorities
What they're looking for
- Product engineering and full-cycle feature ownership
- Developer experience and API design sensibility
- Rapid prototyping and iterative shipping mindset
- Ambiguous problem-solving and self-direction
- Web data extraction or similar domain knowledge
- Backend/full-stack engineering
- Communication with technical audiences
Benefits
- Equity stake up to 0.05%
- 15 days mandatory PTO plus flexible time off beyond 24 days
- 12 weeks fully paid parental leave
- $100/month wellness stipend
- $1,000/year learning and development budget
- 3 months paid sabbatical after 4 years
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Firecrawl
Firecrawl builds a web data extraction platform that transforms URLs into clean, LLM-ready data for AI applications. The company is hiring Forward Deployed Engineers to embed with customers and drive technical delivery, core platform engineers to own the scrape endpoint and product excellence, search and retrieval specialists to build information systems at scale, and Sales Engineers to help technical buyers adopt the platform.
- Website
- firecrawl.dev
Likely interview questions
- Tell us about a developer-facing product you shipped and how you gathered feedback to iterate on it.
- How do you approach turning an ambiguous, messy problem into a clean API surface?