Forward Deployed Engineer

Reposted 18 Days Ago
Be an Early Applicant
San Francisco, CA
In-Office
130K-200K Annually
Junior
Artificial Intelligence • Software
Building embeddings-based search infrastructure
The Role
As a Forward Deployed Engineer, you'll combine engineering and sales skills, work with customers, and develop server solutions. Responsibilities include programming, sales calls, and improving the sales pipeline.
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 a Forward Deployed Engineer at Exa, you'll bridge the gap between engineering and sales by working closely with both our customers and engineering. This requires someone who can program and also sell -- a killer combo!

Desired Experience/Qualities

  • Several years of programming experience (Ex. could build a server in node.js)

  • Have experience talking to customers, whether you were a consultant or a barista

  • Excited to wear many hats (sales, marketing, backend, frontend, etc)

  • Care about the problem of finding high quality knowledge and recognize how important this is for the world

Example Projects

  • Take a series B company from first outbound call to deal closed

  • Build cool coding tutorials demonstrating the Exa API

  • Take many sales calls each week

  • Analyze user interactions and identify ways to improve our sales pipeline

  • Fix bugs in the frontend or backend that customers point out

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

Node.js
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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