Senior Commercial & Product Counsel

🇺🇸 San Francisco, CA
$3K - $3K Annual
Posted 1 day ago
Expires December 20, 2026
Full TimeOn-siteLegal TechIn-House Legal

SENIOR COMMERCIAL & PRODUCT COUNSEL

You'll own Firecrawl's commercial and product legal work end to end. Every enterprise deal, data-licensing agreement, and vendor relationship that gets signed here will run through you, and every product and engineering team shipping into new legal territory will come to you first. This is not a role where you review other people's paper and send back comments. You'll negotiate directly with counterparties, sit next to sales and BD while deals are live, advise engineers on data access and AI questions nobody has settled yet, and build the templates, playbooks, and processes that let the company move faster without taking on risk it shouldn't.

This is the second legal hire at Firecrawl. The General Counsel sets legal strategy, risk posture, and policy. You make it real and make it run. The questions are genuinely novel: who owns AI-generated output, what data access is permissioned and by whom, how liability works for infrastructure that other AI companies build on. Very few lawyers get to work on these this early.

Salary Range: $301,000–$324,000 USD/year, plus eligibility for up to $50,000 USD in semi-annual performance bonuses, totaling up to $100,000 USD annually

Equity Range: Competitive equity. Details shared during the process.

Location: San Francisco, CA (SF HQ). Hybrid, onsite 3+ days a week.

Job Type: Full-Time

Experience: 5–8 years of sophisticated commercial, technology, or product counseling experience

Work Authorization: Open to candidates already authorized to work in the US. We also support H-1B transfers, F-1 OPT and STEM OPT, TN (Canada and Mexico), and E-3 (Australia). We’re not able to sponsor new H-1B visas for candidates outside the US.

ABOUT FIRECRAWL

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.

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