Data Partnerships

Reposted Yesterday
Be an Early Applicant
San Francisco, CA, USA
In-Office
180K-300K Annually
Junior
Artificial Intelligence • Software
Building embeddings-based search infrastructure
The Role
Own the data partnerships cycle, making strategic decisions, negotiating contracts, and driving growth while collaborating with various teams.
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: https://exa.ai/websets


We’re an SF team of ~65 from Harvard, MIT, Apple, Palantir, etc. We recently raised an $85M Series B from Benchmark, and we are rapidly building the most intelligent search engine in history.


Now, we’re looking for someone to help with data partnerships to help us build Exa’s data pipeline!

What You’ll Do

  • Own the data partnerships cycle—from outbound to negotiations to close

  • Make strategic data decisions—our data sets us apart

  • Develop and execute strategies to land high-value partners

  • Work closely with founders, engineers, and go-to-market teams

  • Negotiate contracts, hit targets, and drive growth

  • Eventually, build out a team for Data Partnerships

Kinds of experiences that would help

  • Experience in contract negotiation, ideally having worked with privacy law

  • The ability to pick up highly technical concepts quickly, and speak to it confidently to external stakeholders and potential partners

  • Comfort in a fast-moving, high-growth startup environment

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

Contract Negotiation
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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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