Machine Learning Engineer

Machine Learning Engineer

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.

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.

We hit 8 figures in ARR in year one and more than doubled it in year two. We have 180k+ Git Hub 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 new 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.

• 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 Dev Ops engineers to get models running fast 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

• 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…