We raised a $250M Series C to build the search engine for AIs. Led by a16z, with existing investors Benchmark, Lightspeed, and YC doubling down, the round brings Exa's valuation to $2.2 billion. Read more
Exa is building a search engine from scratch to serve every AI agent. We build massive-scale infrastructure to crawl the web, train state-of-the-art embedding models to process it, and design super high performant vector databases in rust to search over it. If you like compute, we also own a $5M H200 GPU cluster (and soon 5x'ing that) and regularly spin up batchjobs with tens of thousands of machines.
As a Web Crawler engineer, you'd be responsible for crawling the entire web. Basically build Google-scale crawling!
Who You AreYou 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
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). In addition to premium healthcare benefits (medical, dental, vision), we also offer fertility benefits and a monthly wellness stipend to all of our employees.
Skills Required
- Experience building and scaling web crawlers
- Experience with high performance languages like C++ or Rust
- Ability to optimize systems efficiently
- Understanding of high quality knowledge discovery
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.
