Engineering Intern

Reposted 14 Days Ago
Be an Early Applicant
San Francisco, CA
In-Office
100K-150K Annually
Internship
Artificial Intelligence • Software
Building embeddings-based search infrastructure
The Role
As an engineering intern at Exa, you'll engage in various projects related to building a search engine and infrastructure for AI applications, focusing on performance and database optimization.
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 and routinely run batchjobs with 10s of thousands of machines. This isn't your average startup :)

As an engineering intern, you'd play a critical role in lots of our projects. We're pretty flexible on what projects people work on based on their skills and interests.

Desired Experience

  • Currently pursuing a degree in Computer Science, Engineering, Physics, or a related technical field AND are willing to take off a semester to work full time in SF. Remote and part-time interns are not accepted.

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

  • You often code technically difficult or interesting projects for fun

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

Example Projects

  • Recreate google-level keyword search over 10 billion pages in 1 month

  • Build a state-of-the-art crawling system that works optimally for any website

  • Build a custom vector database that runs over a billion vectors in under 100ms

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++
Gpu
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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