Chief of Staff

Reposted 5 Days Ago
San Francisco, CA
In-Office
130K-200K Annually
Mid level
Artificial Intelligence • Software
Building embeddings-based search infrastructure
The Role
The Chief of Staff will manage company strategy, financial modeling, recruitment, and critical business communications while supporting operational efficiency.
Summary Generated by Built In

At Exa, we're building a next-generation AI search engine—one that outperforms Google for power users. We help people find the information they can’t get anywhere else.

We're an SF team of ~35 from Harvard, MIT, Google, Apple etc. We recently raised a Series B from Benchmark, and we are rapidly building the most intelligent search engine in history.

We're looking for a Chief of Staff to join the two cofounders (Will and Jeff) in managing everything from company strategy to financial modeling to legal correspondence to recruiting engineers. You'll work with us everyday to make sure the company is moving as fast as we can toward a very important goal.

Desired Experience

  • Experience being a killer operator who gets shit done well and fast

  • Excited to wear many hats (recruiting, sales, marketing, finance, legal etc.)

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

  • Bonus points if you have some engineering background

Example Projects

  • Support company strategy development

  • Optimize our people operations (e.g., hiring, compensation, etc.)

  • Own critical business communications

  • Prepare board meeting decks

  • Plan and measure company KPIs (e.g., financial modeling, product goals)

We are open to sponsoring International candidates (e.g STEM OPT, OPT, H1B, O1, E3). This is an in-person opportunity in San Francisco. We're big believers in in-person culture!

Top Skills

AI
Financial Modeling
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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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