You’ll build the ML behind Firecrawl: the models and the systems that serve them. That starts with search: training and shipping the ranking and relevance models for one of our fastest-growing products, then extending that work across extraction quality and LLM-driven features. You’ll also own how we measure: A/B testing launches and building the experimentation frameworks the whole team ships against. If you ship models into production, whether your title says ML engineer or data scientist, this is for you.

Salary Range:

$250,000–$290,000 USD/year (SF) / $210,000–$224,000 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 ML or data-heavy systems in production

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.

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 183k+ GitHub 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 current 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. That library is called Alexandria, and it starts now.

What You’ll Do

Improve ranking and relevance for Firecrawl Search, from feature engineering to model training to production

Build and tune models for learning-to-rank, query understanding, and LLM-driven retrieval

Extend ML across Firecrawl's products: extraction quality, content classification, and evaluation of LLM-driven features

Mine query logs and behavioral data at scale to find where our products win and where they fail

Build the data pipelines that turn web-scale crawl and query data into training data and features

Work hands-on with platform, search, and cloud DevOps engineers to get models running rapid and cheap in production

Design our testing strategy:



the A/B testing frameworks and offline evaluation the team ships against

Partner on product launches across Firecrawl: define success metrics, run the experiments, and make the ship/no-ship call on evidence

Report on how releases perform post-launch and turn the findings into the next iteration

What We're Looking For

You've shipped ML models into production systems and owned them after launch: deploying, monitoring, and retraining them, not handing them off

You have real ranking or relevance-modeling experience: learning-to-rank, recommendations, or search quality

You're comfortable in large, data-heavy systems: query logs, pipelines, and datasets that don’t fit in memory

You write production-quality code (Python at minimum) and can work inside a real backend codebase

You're rigorous about measurement. You've designed and analyzed A/B tests and know when a lift is real

You can communicate results clearly to the team: what shipped, what moved, and what to do next

Nice to Have

MLOps experience: MLflow, experiment tracking, model registries, or feature stores. Kubernetes is a plus

Experience building or standardizing an experimentation framework at a previous company

Experience with embedding models, vector retrieval, or LLM-based relevance evaluation

Experience evaluating LLM outputs at scale: quality scoring, structured-extraction accuracy, or agent behavior

Spark or similar large-scale data processing experience

What We're NOT Looking For

A pure statistician or analyst who needs an engineering team to productionize their work

Someone who wants to specialize narrowly and hand off everything else

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