Machine Learning Engineer

Youll 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. Youll 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: $250000$290000 USD/year (SF) / $210000$224000 CAD/year (Toronto)

Equity Range: Competitive equity. Details shared during the process.

Location: San Francisco CA (SF HQ) or Toronto ON (Toronto Hub). Hybrid onsite 3 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. Were not able to sponsor US visas right now. For Canada well 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. Its 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 were 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 187k GitHub stars putting us in the top 40 repositories of all time and developers agents and category-defining AI companies build on us every day. Growth like this is rare and were just getting started.

Were 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. That library is called Alexandria and it starts now.

• 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 Firecrawls 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 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

• Youve 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

• Youre comfortable in large data-heavy systems: query logs pipelines and datasets that dont fit in memory

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

• Youre rigorous about measurement. Youve 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

• 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

• 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

We operate at an absurd level of urgency because the window for what were building wont stay open forever. If that excites you keep reading. If it doesnt no hard feelings but this role probably isnt for you.

• Salary that makes sense: $250000$290000 USD/year (SF) / $210000$224000 CAD/year (Toronto) based on impact not tenure

• Own a piece: Gain competitive equity in what youre 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 USD/month for the gym therapy massages or whatever keeps you human

• Learning & Development: Expense up to $1000 USD/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 partner and kids). No weird loopholes just care that works

• Life & Disability insurance: Employer-paid basic life and AD&D short-term disability and long-term disability. Coverage for lifes curveballs

• Virtual care and a health guide: Teladoc for the couch doctor visit plus Rightway to answer coverage questions and fight billing errors for you

• Mental health: Talkspace therapy and psychiatry on your schedule

• Fertility and family building: Carrot covering you and your partner

• EAP: Free confidential counseling legal and financial consults and online will prep through Guardian

• 401(k) plan: Retirement might be a ways off but future-you will thank you

• Pre-tax benefits: HSA FSA and commuter benefits to help your wallet out a bit

• Supplemental options: Extra life and AD&D accident critical illness hospital indemnity plus pet legal and identity protection through MetLife

• Full coverage no red tape: Extended health dental and vision through Manulife (Diamond the top tier) 100% employer-paid for you your partner and your kids

• Life & Disability insurance: Employer-paid life AD&D short-term disability and long-term disability. Coverage for lifes curveballs

• Virtual care: Dialogue Premium so you can see a doctor or nurse from your couch any hour

• Mental health: Talkspace Elite therapy and psychiatry on your schedule

• Fertility and family building: Carrot covering you and your partner

• Retirement: Group RRSP through Wealthsimple so future-you can thank you

• 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

• Toronto Hub perks: Snacks drinks team lunches glass-walled views down University Avenue and a home base steps from Union Station

• Transit covered: A PRESTO card loaded for GO Transit subway and streetcar plus station parking if you drive to the train. Winter-proof on us

Application Review: Send us your work and a quick note on why this excites you. Show us what youve built: ranking models search systems experimentation frameworks pipelines that fed production models. We care about what youve shipped not where you went to school.

Intro Chat (25 min): A quick conversation to get to know each other before we go deep. Well talk about what youve been working on what drew you to Firecrawl and what youre looking for in your next role. Time for your questions too.

Technical Chat (45 min): Well dig into a real problem from our world (improving ranking quality with noisy relevance signals designing the A/B test for a product launch building features from query logs) and talk through how youd 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 (40 Hours): Work with the team on a real scoped piece of the product paid at a contractor rate. Its the truest signal for both sides. You see what building at Firecrawl actually feels like and we see how you ship. Remote-friendly and well 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.

Required Experience:

IC