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.
We recently raised an $85M Series B from Benchmark, and we are rapidly building the most intelligent search engine in history. We’re high agency, low-ego, and united by the feeling that this is one of the last problems worth getting right.
Generalist Research Engineers work across our search and retrieval stack: crawling, parsing, ML performance, retrieval algorithms. The team is centered around improving end quality of search endpoints for customers.
Who You Are
High attention to detail; simultaneously understand customer needs and implementation details of the system
Great intellectual horsepower - generalists who are eager to go deep
You're excited to work across our search and retrieval stack: crawling, parsing, ML performance, retrieval algorithms and more
Comfortable working across different systems, and shipping fast
You care about the problem of finding high quality information and recognize how important this is for the world
What You Could Do
Design and ship a new parser that dramatically improves retrieval performance
Improve throughput and latency of ML models
Make our web crawler 3x faster
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
- Extensive experience with React and Node.js
- Prompt engineering experience
- Ability to work on varied projects across sales, marketing, backend, and frontend
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.
