Machine Learning Engineer

📋 Job Overview

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.

🏢 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 - 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 170k+ GitHub stars, 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.

🎯 The Role

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 engineers and cloud DevOps to get models running fast and cheap in production. Design and formulate 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.

✅ Key Responsibilities

• 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 engineers and cloud DevOps to get models running fast and cheap in production
• Design and formulate 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

📌 Required Qualifications

• 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

⭐ Desirable Experience

• 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

🎁 Benefits

Salary that makes sense — $210,000–$240,000/year, based on impact, not tenure. Own a piece — Gain competitive equity in what you're helping build. Generous PTO — 15 days mandatory, anything after 24 days, just ask (holidays excluded); take the time you need to recharge. Parental leave — 12 weeks fully paid, for all parents. Wellness stipend — $100/month for the gym, therapy, massages, or whatever keeps you human. Learning & Development — Expense up to $1,000/year toward anything that helps you grow professionally. Team offsites — A change of scenery, minus the trust falls. Sabbatical — 3 paid months off after 4 years, do something fun and new. Full coverage, no red tape — Medical, dental, and vision (100% for employees, 50% for spouse/kids) — no weird loopholes, just care that works. Life & Disability insurance — Employer-paid short-term disability, long-term disability, and life insurance — coverage for life's curveballs. Supplemental options — Optional accident, critical illness, hospital indemnity, and voluntary life insurance for extra peace of mind. Doctegrity telehealth — Talk to a doctor from your couch. 401(k) plan — Retirement might be a ways off, but future-you will thank you. Pre-tax benefits — Acc

🛂 Visa & Eligibility

Must be legally authorized to work in the United States. We're not able to sponsor visas right now, though that may change down the line.