À propos de ce poste Machine Learning Engineer chez Firecrawl
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: $210,000–$240,000/year
Equity Range: Competitive equity — details shared during the process.
Location: San Francisco, CA (Hybrid, on-site required)
Job Type: Full-Time
Experience: 3+ years building ML or data-heavy systems in production
Visa: 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.
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.
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 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
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
Someone who optimizes for process over shipping
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.
Benefits & Perks
Available to all employees
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
Available to US-based full-time employees
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 — Access to FSAs and commuter benefits (US-only) to help your wallet out a bit
Pet insurance — Because fur babies are family too
Available to SF-based employees
SF HQ perks — Snacks, drinks, team lunches, intense ping pong, and peak startup energy
E-Bike transportation — A loaner electric bike to get you around the city, on us
Interview Process
Application Review — Send us your work and a quick note on why this excites you. Show us what you've built — search systems, indexing pipelines, ranking improvements. We care about what you've shipped, not where you went to school.
Intro Chat (~25 min) — A quick conversation to get to know each other before we go deep. We'll talk about what you've been working on, what drew you to Firecrawl, and what you're looking for in your next role. Time for your questions too.
Technical Chat (~45 min) — We'll dig into a real problem from our world; examples include: improving ranking quality with noisy relevance signals, designing the A/B test for a product launch, or building features from query logs — and talk through how you'd approach it. Come ready to think out loud; we care how you reason, not whether you memorized the answer.
Founder Chat (~25 min) — Culture, pace, ownership, and how you like to work. Time for your questions too.
Paid Work Trial (1-2 weeks) — Work with the team on a real, scoped piece of the product — paid at a contractor rate. It's the truest signal for both sides: you see what building at Firecrawl actually feels like, and we see how you ship. Remote-friendly, and we'll flex around your current commitments.
Decision — We move fast after the trial.
If you want your models ranking results for the whole web — and to see the impact in production the same week — you should join us.
👉 Apply now.
