Marketing

Reposted 11 Days Ago
Be an Early Applicant
San Francisco, CA
In-Office
120K-220K Annually
Mid level
Artificial Intelligence • Software
Building embeddings-based search infrastructure
The Role
Join Exa's Marketing team to drive enterprise marketing efforts, manage multi-channel campaigns, and develop insightful materials to support sales initiatives.
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 ~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.

As a Marketing team member at Exa, you will work with customers, partners, and our eng team to communicate the product and grow our business!

Desired Experience/Qualities

  • You have deep experience in enterprise marketing, ideally at high-growth SaaS or AI companies where sales cycles are long, deals are large, and messaging needs to land with executives.

  • You’ve run multi-channel campaigns across events, content, paid media, and ABM (account-based marketing) to move deals forward and generate enterprise pipeline.

  • You’re comfortable partnering with sales leadership, building enablement materials, case studies, and deal-specific campaigns that help close six- and seven-figure contracts.

  • You have a data-driven approach: you use metrics like pipeline contribution, ACV influence, and win rates to measure impact and guide iteration.

  • You care deeply about building perfect search and are excited about advocating for Exa :)

This is an in-person opportunity in San Francisco. We're big believers in in-person culture!

Top Skills

AI
Marketing Analytics
SaaS
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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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