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Urban Ridge Supplies

Machine Learning Engineer

Toronto, ON, Canada

Job type: Full-time
Experience: Experience: 3+ years building ML or data-heavy systems in production
Posting language: English
Posted: Sep 24, 2026

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Original job description

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). On-site, five 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. We're not able to sponsor US visas right now. For Canada, we'll consider sponsorship on a case-by-case basis 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 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. Growth like this is rare, and we're just getting started.

We're a small team punching far above our weight, working out of SF HQ and our new Toronto Hub. 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.

Job Id: nVAtmzVJj4FR+ESlvp9jGXNwDP5AQkmM13Z3hL3d6GnTM3lZOPkcpt10fbRPfsrVW/mL88w+9/6jpS2Y8nOAsp9fTqR7zdRSew==

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More about this job

Responsibilities

You will build and maintain machine learning models for search, ranking, and relevance, while developing experimentation frameworks to measure performance. Additionally, you will collaborate with engineering teams to deploy these models into production and optimize data pipelines for web-scale processing.

Requirements

Candidates must have at least 3 years of experience building and maintaining production-grade machine learning or data-heavy systems. Proficiency in Python and experience with ranking, relevance modeling, or large-scale data processing is required.

Skills

Machine Learning
Python
Ranking models
Relevance modeling
A/B testing
Experimentation frameworks
Data pipelines
Production systems
LLM
Vector retrieval
Embedding models
Query understanding
Feature engineering
Model training
Data analysis
MLOps

Benefits

Competitive equity
Generous PTO
Parental leave
Wellness stipend
Learning & Development
Medical insurance
Dental insurance
Vision insurance
Life insurance
Disability insurance
Mental health support
Fertility and family building
401(k) plan
Retirement plan
Commuter benefits
Sabbatical

Visa sponsorship

Not detected in the job text

Keywords

Machine Learning
Python
Ranking
Relevance
LLM
A/B Testing
Data Pipelines
MLOps
Kubernetes
MLflow
Feature Stores
Vector Retrieval
Spark
DevOps
Search Quality
Query Logs
Structured Data
Markdown
API
Production
Experimentation
Model Training
Embedding Models
Data Engineering
Software Engineering

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