Software Engineer, Backend

Reposted 3 Days Ago
San Francisco, CA
In-Office
150K-300K Annually
Entry level
Artificial Intelligence • Software
Building embeddings-based search infrastructure
The Role
As a Backend Engineer, you will work on various backend systems, optimizing performance and building high-scale infrastructure for a search engine.
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 backend engineer, you'd play a critical role in our backend system. We're pretty flexible on what projects people work on based on their skills and interests.

Desired Experience

  • You have experience writing and maintaining high throughput, low latency systems

  • You can build data processing pipelines that deal with millions of documents per day

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

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

  • Plus: Experience in some high performance language (C++, Rust, etc.)

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). In addition to premium healthcare benefits (medical, dental, vision), we also offer fertility benefits and a monthly wellness stipend to all of our employees.

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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