Success Engineer

You'll be the technical partner customers count on for the long haul. Once an account is live on Firecrawl, you're the one who keeps them winning: deepening how they use the product, unblocking the hard technical problems that come up, and turning a working integration into a growing one. This is a customer-facing engineering role measured in adoption, retention, and expansion, not tickets closed. You build, you deploy, you own the outcome.

Salary Range: $209,000–$265,000 USD/year OTE

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

Location: San Francisco, CA (SF HQ). On-site, five days a week.

Job Type: Full-Time

Experience: 3+ years in a customer-facing technical role or equivalent

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.

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 192k+ 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.

Our Values

Bias to action: We move fast, ship hard, and don’t wait to be told.

Realistic about AI: We’re clear-eyed about what AI can and can’t do, and we keep pushing to use it where it actually works.

All in: The next few years matter a lot, and we’re all in for them.

Human connection: As intelligence gets cheaper, relationships with customers, partners, and each other matter more.

What You'll Do

Own the technical health of your accounts: keep customers succeeding, deepen their usage, and catch problems before they become churn

Get hands-on with the product against real customer data, build integrations, and solve the technical problems that keep accounts growing

Run technical discovery: diagnose where a customer's extraction or pipeline breaks, and know whether it's usage, a bug, or a real product gap

Turn technical wins into adoption and expansion, and partner with the Deployment team to hit team targets

Be the trusted technical voice for your accounts over their whole lifecycle, and bring what you learn in the field back to product and engineering

Know when to solve it yourself and when to pull in engineering

What We're Looking For

You've done customer success engineering, solutions, forward-deployed, or another customer-facing technical role, ideally at a developer, API, or AI-infra product

You're technical enough to earn a developer's respect: you can read code, use an API, and build a real POC or integration yourself

You've owned accounts over time and grown them, and can point to the technical work that drove adoption and expansion

You check before you build: you can tell user error from a bug from a real product gap, and you don't turn every ask into a custom feature

You can translate between deep technical detail and business value without losing either audience

You move fast and close the loop. You'd rather ship a working fix against real data than perfect a doc