About Exa
We raised a $250M Series C to build the search engine for AIs. Led by a16z, with existing investors Benchmark, Lightspeed, and YC doubling down, the round brings Exa's valuation to $2.2 billion. Read more
Exa is building a search engine for the AI era. Our Search API currently powers Agents, Fortune 500s and AI labs as we transform an industry that hasn’t been disrupted since the 90s. We're a largely SF-based team of ~100 from Harvard, MIT, Meta, Google Research, ex-founders & dropouts alike.
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.
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!
Who You Are
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
What You'll Do
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). 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
- Several years of programming experience
- Experience talking to customers
- Ability to work in multiple roles (sales, marketing, etc.)
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.
