Software Engineer, Web Crawling

Reposted 5 Days Ago
Hiring Remotely in USA
Remote
150K-300K Annually
Mid level
Artificial Intelligence • Software
Building embeddings-based search infrastructure
The Role
As a Web Crawler Engineer, you will develop a search engine by crawling the web, optimizing systems, and scaling infrastructure for high performance and efficiency.
Summary Generated by Built In

Exa is building a search engine from scratch to serve every AI application. We build massive-scale infrastructure to crawl the web, train state-of-the-art embedding models to index it, and develop super high performant vector databases in Rust to search over it. We also own a $5M H200 GPU cluster that regularly lights up tens of thousands of machines.

As a Web Crawler engineer, you'd be responsible for crawling the entire web. Basically build Google-scale crawling!

Desired Experience
  • You have extensive experience building and scaling web crawlers, or would be excited to ramp up very quickly

  • You have experience with some high performance language (C++, Rust, etc.)

  • You are familiar with TypeScript, Playwright, modern web design, CDP (Chrome DevTools Protocol)

  • You’re comfortable optimizing a system to an exceptional degree

  • You care about the problem of finding high quality knowledge and recognize how important this is for the world

Example Projects
  • Build a distributed crawler that can handle 100M+ pages per day

  • Optimize crawl politeness and rate limiting across thousands of domains

  • Design systems to detect and handle dynamic content, JavaScript rendering, and anti-bot measures

  • Create intelligent crawl scheduling and prioritization algorithms for maximum coverage efficiency

This is an in-person opportunity in San Francisco. We're happy to sponsor international candidates (e.g., STEM OPT, OPT, H1B, O1, E3).

Top Skills

C++
Rust
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The Company
San Francisco, , California
86 Employees
Year Founded: 2021

What We Do

Exa was built with a simple goal — to organize all knowledge. After several years of heads-down research, we developed novel representation learning techniques and crawling infrastructure so that LLMs can intelligently find relevant information.

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