Machine Learning Engineer

Firecrawl•San Francisco, CA

39d•$210,000 - $240,000•Hybrid

About The Position

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. A Note On Pace We operate at an absurd level of urgency because the window for what we're building won't stay open forever. If that excites you, keep reading. If it doesn't, no hard feelings — but this role probably isn't for you.

Requirements

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
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.

Nice To Haves

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

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

Benefits

Salary that makes sense — $210,000–$240,000/year, based on impact, not tenure
Gain competitive equity in what you're helping build
15 days mandatory PTO, anything after 24 days, just ask (holidays excluded); take the time you need to recharge
12 weeks fully paid parental leave, for all parents
$100/month wellness stipend for the gym, therapy, massages, or whatever keeps you human
Expense up to $1,000/year toward anything that helps you grow professionally
Team offsites
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
Employer-paid short-term disability, long-term disability, and life insurance — coverage for life's curveballs
Optional accident, critical illness, hospital indemnity, and voluntary life insurance for extra peace of mind
Talk to a doctor from your couch
Retirement might be a ways off, but future-you will thank you
Access to FSAs and commuter benefits (US-only) to help your wallet out a bit
Because fur babies are family too
Snacks, drinks, team lunches, intense ping pong, and peak startup energy
A loaner electric bike to get you around the city, on us