AI Enablement Lead

San Francisco · USD 235,000-260,000 / year · 4+ years experience · Full-time · Hybrid · Posted Oct 5, 2026

At a glance

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 2026, we raised a $75M Series B led by Smash Capital, and we're spending it building the largest repository of knowledge in the world. We hit 8 figures in ARR in year one and more than doubled it in year two. We have 187k+ GitHub stars, putting us in the top 40 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. 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. That library is called Alexandria, and it starts now.

The AI Enablement Lead will own the internal AI stack, including agent harnesses, configurations, and model routing to ensure company output scales with token usage.
This role requires a blend of technical engineering and high emotional intelligence to drive the adoption of AI agents across internal teams.
The position involves analyzing token spend data to optimize workflows and teaching team members how to integrate AI agents into their daily work.

Own the internal AI stack, including agent harnesses, configs, shared skills, and model routing.
Identify and implement AI agent workflows for the revenue team and other departments.
Onboard team members to AI tools, providing hands-on support to overcome skepticism and resistance.
Analyze token spend data to identify inefficiencies and optimize cost per useful token.
Manage model choice, routing, and caching to ensure workflows are cost-effective.
Build internal tooling, including MCP servers, hooks, and evals, to address gaps in existing harnesses.
Stay current with developments in tools like Claude Code, Cursor, and Codex to maintain operational efficiency.
Educate teams on the impact of scaling with tokens and how it relates to their specific work.

4+ years of relevant experience.
Proven track record of rolling out coding agents or AI tooling across a team.
Technical proficiency in writing code and building internal tooling.
Experience analyzing spend data to drive operational changes.
Ability to explain technical AI concepts to diverse stakeholders including sales, legal, and engineering.
Must be authorized to work in the United States.
Ability to work onsite in San Francisco 3+ days a week.