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. 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. 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

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.

In September 2026 we raised a $75M Series B led by Smash Capital, and we’re spending it building the largest repository of knowledge in the world. We have 183k+ GitHub stars, putting us in the top 50 repositories of all time, and developers, agents, and category-defining AI companies build on us every day. We’re a small team punching far above our weight, working out of SF HQ and our new Toronto Hub. 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

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 DevOps engineers to get models running rapid 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

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'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

Experience building or standardizing an experimentation framework at a previous company

Experience evaluating LLM outputs at scale: quality scoring, structured-extraction accuracy, or agent behavior

Spark or similar large-scale data processing experience

Someone who optimizes for process over shipping

We operate at an absurd level of urgency because the window for what we're building won't stay open forever. 250,000–$290,000 USD/year (SF) / $210,000–$224,000 CAD/year (Toronto), based on impact, not tenure

Gain competitive equity in what you're helping build

Generous PTO: 15 days mandatory, anything after 24 days, just ask (holidays excluded). Parental leave: 12 weeks fully paid, for all parents





Expense up to $1,000 USD/year toward anything that helps you grow professionally

Available to US-based full-time employees

- Medical, dental, and vision (100% for employees, 50% for partner and kids). Life & Disability insurance: Employer-paid basic life and AD&D;, short-term disability, and long-term disability. Virtual care and a health guide: Fertility and family building: EAP: Free confidential counseling, legal and financial consults, and online will prep through Guardian
- 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
- Extra life and AD&D;, accident, critical illness, hospital indemnity, plus pet, legal, and identity protection through MetLife

Available to Canada-based full-time employees

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. Virtual care: Fertility and family building: Retirement: Toronto Hub perks: Snacks, drinks, team lunches, glass-walled views down University Avenue, and a home base steps from Union Station

Show us what you've built: ranking models, search systems, experimentation frameworks, pipelines that fed production models. We care about what you've shipped, not where you went to school.

Intro Chat (~25 min)

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. Technical Chat (~45 min)

We'll 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 you'd approach it. Founder Chat (~25 min)

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. Remote-friendly, and we'll flex around your current commitments.

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.

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📌 Machine Learning Engineer, Full-time (Toronto)
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