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Added 15 mins ago
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Mendable

Research Engineer - Benchmarks

Toronto, ON, Canada

Pay: $250,000 to $290,000 USD / year
Job type: Full-time
Apply by: Oct 30, 2026
Experience: Experience: 4+ years in ML, research engineering, or data engineering, with evaluation or benchmark work you've shipped
Posting language: English
Posted: Sep 30, 2026

Apply About employer

Applications open in a new tab.

Original job description

Every week, someone asks which data provider is actually best: for company records, for finding the right person, for fresh job listings. Right now the answers come from the vendors themselves. You'll build the independent version. You'll run rigorous, automated, public benchmarks of the providers in Alexandria and beyond, and publish them weekly. The same results will feed straight back into Alexandria so it learns which provider to call for which job.

This isn't our internal evals role. You're measuring the market, in public, where every number will be challenged by the vendors it ranks. You'll own it end to end: the datasets, the ground truth, the scoring, the harness, the weekly release, and the loop into Alexandria's provider selection. No one hands you a methodology. You write it, defend it, and ship it every week.

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.

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

Location: San Francisco, CA (SF HQ). On-site, five days a week.

Job Type: Full-Time

Experience: 4+ years in ML, research engineering, or data engineering, with evaluation or benchmark work you've shipped

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 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

Design and run head-to-head benchmarks of data providers against verified ground truth. For example:
Build and maintain the test datasets and ground truth, and keep them from going stale or leaking.
Own the automated harness and the weekly benchmark release. Every run has to be reproducible, versioned, and defensible.
Measure what buyers actually care about: accuracy, coverage, freshness, speed, and cost.
Work with marketing to ship public leaderboard pages that are useful and hold up under scrutiny.
Close the loop into Alexandria so benchmark results change which provider gets called for what.
Keep expanding the categories we benchmark, inside Alexandria and beyond it.

What We're Looking For

You've shipped evals or benchmarks, and you can explain exactly why your results were trustworthy.
You've done this somewhere that matters: a frontier lab, a data company like Scale, Surge, micro1 or Mercor, a third-party benchmark org, or a public benchmark project. OSS contributors very welcome.
You're strong in Python and API integrations, and comfortable with messy vendor APIs, rate limits, and inconsistent schemas.
You know how to build test sets and scoring methods: sampling, labeling, inter-rater agreement, and when to trust an LLM judge and when not to.
You use AI heavily and keep upgrading your own workflow. You ship without waiting for instructions.
You write findings clearly for engineers, marketers, and the vendors on the other side of the leaderboard.

What We're Not Looking For

Someone who wants to run benchmarks someone else designed.
Someone who picks the metric that makes the story look good. Our numbers have to survive the vendors who lose.
A pure researcher who won't build the harness, or a pure engineer who won't think hard about methodology.
Someone who needs a fully specced ticket, or a quarter, to ship the first result.

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, / $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. We want the benchmark, leaderboard, eval, or dataset you built, and how you knew it was right. 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. We'll cover what you've been working on, what drew you to Firecrawl, and what you want next. Time for your questions too.
Technical Chat (~45 min): A real problem from our world: design a benchmark that decides whether FullEnrich or DataLegion finds the right person's current title. We'll cover where the ground truth comes from and how you'd defend the result to the vendor who loses. Come ready to think out loud.
Workflow Chat (~30 min): Show us how you actually work: your AI tools, your setup, and a recent thing you shipped faster than you would have a year ago.
Founder Chat (~25 min): Culture, pace, ownership, and how you like to work. Time for your questions too.
Paid Work Trial (~40 hours): Ship a small benchmark end to end on a real provider category, paid at a contractor rate. It's the truest signal for both sides. Remote-friendly, and we'll flex around your current commitments.
Decision: We move fast after the trial.

If you want to be the person the whole market checks before picking a data provider, you should join us.

👉 Apply now.

Apply About employer

More about this job

Responsibilities

Design, build, and publish reproducible weekly benchmarks comparing data providers against verified ground truth, including datasets, scoring methods, and automated testing infrastructure. Share results publicly and integrate benchmark findings into Alexandria's provider-selection process.

Requirements

Applicants need at least four years of experience in machine learning, research engineering, or data engineering, including shipped evaluation or benchmark work, and strong Python and API integration skills. They should be able to develop defensible datasets and scoring methods, communicate findings clearly, and independently deliver work in a fast-paced environment.

Skills

Benchmark Design
Evaluation Methodology
Python
API Integration
Data Engineering
Machine Learning
Dataset Development
Ground-Truth Validation
Statistical Sampling
Data Labeling
Inter-Rater Agreement
Scoring Methods
Automated Testing Harnesses
Vendor API Management
Technical Writing
Reproducible Research

Benefits

Competitive Equity
Paid Time Off
Paid Parental Leave
Wellness Stipend
Learning And Development Budget
Paid Sabbatical
Medical Insurance
Dental Insurance
Vision Insurance
Life And Disability Insurance
Virtual Healthcare
Mental Health Support
Fertility And Family-Building Benefits
Employee Assistance Program
401(K) Plan
Health Savings Account
Flexible Spending Account
Commuter Benefits
Extended Health Insurance
Group RRSP
Team Meals
Transit Pass
E-Bike Transportation

Visa sponsorship

Detected

Keywords

Python
Machine Learning
Research Engineering
Data Engineering
Benchmarking
Evaluation
Data Providers
Ground Truth
Test Datasets
Automated Harness
Reproducibility
Versioning
Accuracy
Coverage
Data Freshness
Latency
Cost Measurement
API Integration
Vendor APIs
Rate Limits
Schema Inconsistency
Sampling
Data Labeling
Inter-Rater Agreement
LLM Judge
Public Leaderboards
Provider Selection
Alexandria
FullEnrich
DataLegion

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