---
title: 'Machine Learning Engineer at Firecrawl'
canonical: 'https://feeny.ai/job/machine-learning-engineer-firecrawl-san-francisco-a9qf005tt1fm'
type: 'job'
last_seen: '2026-10-13'
---

# Machine Learning Engineer at Firecrawl

- **Company:** Firecrawl
- **Location:** San Francisco, CA / Toronto, Canada
- **Compensation:** $250k–$290k
- **Employment:** full-time
- **Work type:** hybrid
- **Posted:** 2026-08-20
- **Last confirmed live:** 2026-10-13
- **Apply:** https://jobs.ashbyhq.com/firecrawl/72f9dc1d-65db-48c9-b3d9-c6ccdb997006

## Job description

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: $250,000–$290,000 USD/year (SF) / $210,000–$224,000 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: Open to candidates already authorized to work in the US or Canada. In the US, we also support H-1B transfers, F-1 OPT and STEM OPT, TN (Canada and Mexico), and E-3 (Australia). We’re not able to sponsor new H-1B visas for candidates outside the US. For Canada, we’ll consider work permit sponsorship case by case through our Toronto Hub.

## 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. 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 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 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. That library is called Alexandria, and it starts now.

## 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, 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

## 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: $250,000–$290,000 USD/year (SF) / $210,000–$224,000 CAD/year (Toronto), 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 USD/month for the gym, therapy, massages, or whatever keeps you human
- Learning & Development: Expense up to $1,000 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

Available to US-based full-time employees

- 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 life's 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

Available to Canada-based full-time employees

- 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 life's 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

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

Available to Toronto-based employees

- 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

Interview Process Application Review: Send us your work and a quick note on why this excites you. 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): 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 (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. 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. 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.

## About Firecrawl

## Company Overview
- **One-liner**: Firecrawl provides an API that allows AI systems to search, scrape, and interact with web content at scale, converting it into clean, structured data for LLMs and AI-native software.
- **Entity Type**: Private (Series A)
- **Headquarters**: San Francisco, California, USA
- **Founded**: 2022
- **Founders**: Caleb Peffer (CEO), Eric Ciarla (Chief Growth Officer), Nicolas Silberstein Camara (CTO)

## Core Business
- **Primary industry**: Infrastructure for AI / Web Data Extraction
- **Target customers**: B2B, developers, AI engineers, data scientists, and companies building LLM-based applications and agents
- **Mission statement**: "To make web data work for AI — cleanly, scalably, and in real time." The company describes itself as "building the eyes for AGI."

## Products & Services
- **[Firecrawl API](https://www.firecrawl.dev/)**: A unified API that turns the live internet into clean Markdown and structured data for LLMs, agents, and AI-native software. Core capabilities include:
  - **Search**: Searches the web and returns full page content (not just links) for real-time context and RAG pipelines.
  - **Scrape**: Converts any URL into clean markdown, structured JSON, screenshots, or semantic text.
  - **Interact**: Cloud sandboxes for agents that can click buttons, fill forms, and navigate dynamic content using natural language or code.
  - **Crawl**: Crawls websites at scale with blazing speed (P95 latency of 3.4s) and industry-leading reliability (covers 96% of the web, including JS-heavy pages).

## Market Standing
- **Valuation/Market Cap**: Not publicly disclosed
- **Key Metric**: Total funding of **$16.2M** (Seed from Y Combinator, Series A led by Nexus Venture Partners)
- **Notable Investors/Partners**: Y Combinator, Nexus Venture Partners (Lead, Series A), Abhinav Asthana (CEO of Postman), Tobias Lutke (CEO of Shopify), Matt McClure (Founder of Mux)
- **Growth Signals**:
  - One of the fastest growing open source projects in the world with **125K+ GitHub stars** (one of the top 100 repos on GitHub)
  - **1.25M+ weekly downloads**
  - **150K+ developers signed up**
  - **5B+ requests served**
  - **125K+ companies using Firecrawl**
  - The company states it plans to hit **$100M in ARR with fewer than 50 people**

## Competitive Advantages
- **Unified platform**: Unlike duct-taping together proxies, scrapers, orchestrators, and post-processing logic, Firecrawl offers a single API for search, scrape, crawl, and interact — purpose-built for AI.
- **Speed and reliability**: P95 latency of 3.4s and 96% web coverage, including JavaScript-heavy pages, making it production-ready for real-time agents.
- **Open source dominance**: Being one of the top 100 repos on GitHub creates massive community adoption, trust, and a built-in distribution channel.
- **Founder-market fit**: The team previously built Mendable and SideGuide, giving them deep experience in the AI and developer tooling space.

## Strategic Focus
- **Current priorities**: Scaling the platform to handle increasing demand, expanding the "Interact" capability for agentic workflows, and maintaining leadership in the web-to-LLM data pipeline space.
- **Direction for growth**: Building the foundational infrastructure for AI-native software where "intelligence isn't an add-on, it's part of the core." The company is hiring aggressively across engineering, research, and growth roles to achieve its $100M ARR target with a lean team.

## Why Work Here
- **Culture**: Founder-led environment — 80% of the team are former founders, 80% have a developer background. The company values "extreme speed" and ships rapidly.
- **Remote/Hybrid policy**: Hybrid in San Francisco, CA OR Remote in the Americas (UTC-3 to UTC-10). Full-time positions with visa sponsorship available.
- **Team size**: Lean team of approximately 12 people currently, with a philosophy of staying small and efficient.
- **Notable perks**: Opportunity to work on a high-impact, open-source project that is foundational to the AI ecosystem. The company is backed by top-tier investors and has massive traction, offering significant growth potential for early employees.
- **Engineering culture**: Emphasis on building the "eyes for AGI" — working at the cutting edge of AI infrastructure, with roles spanning product engineering, research engineering (Search/IR, Reinforcement Learning, Evals), and growth engineering.

## Sources
1. [firecrawl.dev/about](https://www.firecrawl.dev/about)
2. [firecrawl.dev](https://www.firecrawl.dev/)
3. [firecrawl.dev/careers](https://www.firecrawl.dev/careers)
4. [trymatchbox.com](https://www.trymatchbox.com/startups/70a1509f-8242-4a51-83ee-516a9ce596d5)
5. [github.com/firecrawl/firecrawl](https://github.com/firecrawl/firecrawl)

## Other roles at Firecrawl

- [Partnerships Manager - Alexandria](https://feeny.ai/job/partnerships-manager-alexandria-firecrawl-san-francisco-nrheeczf8k0j) — San Francisco, CA
- [AI Enablement Lead](https://feeny.ai/job/ai-enablement-lead-firecrawl-san-francisco-4a49rq1vbn2d) — San Francisco, CA
- [Chief Financial Officer](https://feeny.ai/job/chief-financial-officer-firecrawl-san-francisco-3jwwfavz4j9s) — San Francisco, CA
- [Research Engineer - Benchmarks](https://feeny.ai/job/research-engineer-benchmarks-firecrawl-san-francisco-yr374dbkm5tp) — San Francisco, CA / Toronto, Canada
- [Support Engineer](https://feeny.ai/job/support-engineer-firecrawl-san-francisco-shaj3nx8sskc) — San Francisco, CA / Toronto, Canada
- [Legal Operations Manager](https://feeny.ai/job/legal-operations-manager-firecrawl-san-francisco-65hykameaepv) — San Francisco, CA / Toronto, Canada
- [Open Source Engineer](https://feeny.ai/job/open-source-engineer-firecrawl-san-francisco-tr94p135ysae) — San Francisco, CA / Toronto, Canada
- [Agent Experience Engineer](https://feeny.ai/job/agent-experience-engineer-firecrawl-san-francisco-vzk5eax5yxrg) — San Francisco, CA / Toronto, Canada
- [Social Video Producer](https://feeny.ai/job/social-video-producer-firecrawl-san-francisco-334dkavm0dvd) — San Francisco, CA
- [Web Automation Engineer](https://feeny.ai/job/web-automation-engineer-firecrawl-san-francisco-xv8q3rdbrmv9) — San Francisco, CA / Toronto, Canada