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Firecrawl

Web-data platform for AI agents

Research Engineer - Benchmarks

AI ResearchUpdated on Nov 11
Full-time

About the Job

Requirements

  • Candidates should have shipped evaluations or benchmarks and be able to explain why their results were trustworthy.
  • Candidates should have relevant experience at a frontier lab, a data company such as Scale, Surge, micro1, or Mercor, a third-party benchmark organization, or on a public benchmark project; open-source contributors are welcome.
  • Candidates should be strong in Python and API integrations, and comfortable with vendor APIs, rate limits, and inconsistent schemas.
  • Candidates should know how to build test sets and scoring methods, including sampling, labeling, inter-rater agreement, and when to trust an LLM judge.
  • Candidates should use AI heavily and continually improve their workflow.
  • Candidates should be able to communicate findings clearly to engineers, marketers, and vendors.
  • The role requires 4+ years in machine learning, research engineering, or data engineering, with evaluation or benchmark work shipped.

Responsibilities

  • Design and run head-to-head benchmarks of data providers against verified ground truth, including comparisons of company details, person and employment information, and relevant, fresh, non-duplicate job listings.
  • Build and maintain test datasets and ground truth, and prevent them from going stale or leaking.
  • Own the automated harness and weekly benchmark release, ensuring every run is reproducible, versioned, and defensible.
  • Measure accuracy, coverage, freshness, speed, and cost.
  • Work with marketing to publish public leaderboard pages that are useful and withstand scrutiny.
  • Feed benchmark results into Alexandria so they affect provider selection.
  • Expand the benchmark categories inside Alexandria and beyond it.

Desired Qualifications

  • Experience at a frontier lab, data company, third-party benchmark organization, or public benchmark project is presented as a preferred background; open-source contributors are welcome.

About the Company

Firecrawl

Web-data platform for AI agents

Firecrawl provides a context API for AI agents to search, crawl, scrape, and retrieve clean content from the live web and structured indexes at scale. Its platform handles crawling, rendering, parsing, and cleanup so teams don’t need to build their own extraction pipelines. The Alexandria layer unifies official data providers, custom connectors, and Firecrawl indexes with the live web, enabling agents to discover sources and pull knowledge, including paid access like Wikimedia Enterprise. The service supports scale to millions of users and funds deeper indexes, search capabilities, and knowledge-contributor payouts.

Company size
51-200
Company stage
Late Stage VC
Total funding
$89.5M
Headquarters
San Francisco, CA
Founded
2022

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