Research Engineer, Generalist

Reposted 3 Days Ago
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Singapore
In-Office
Entry level
Artificial Intelligence • Software
Building embeddings-based search infrastructure
The Role
As a Research Engineer, you'll work on building and improving a scalable search engine, focusing on crawling, parsing, ML performance, and retrieval algorithms.
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.

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.


Desired Experience

  • 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


Example Projects

  • 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 Singapore. We’re happy to sponsor international candidates. 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

Machine Learning
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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