Exa is an applied AI lab building a search engine unlike the world has ever seen. We build massive-scale infra to crawl the entire web, train state-of-the-art embedding models to process it, and design super high performant vector databases to retrieve over it. We now power search for Cursor, Cognition, HubSpot, and over 400,000 developers and have raised $350m from Lightspeed, Benchmark, and a16z.

Our ultimate goal is to build perfect search over all the world's information, far beyond Google. If you want to build massive-scale ML systems that will define the way the new AI world consumes information, this is the place for you.

Evals at Exa

The Evals Team sits at the heart of our mission. We train foundational models for search. Our goal is to build systems that can instantly filter the world's knowledge to exactly what you want, no matter how complex your query. Basically, put the web into an extremely powerful database. And to do that well, we need to measure what “good search” actually means. That’s where you come in.

As a Product Manager on the Evals team, you will partner with research, product, GTM, and engineering to oversee the quality and completeness of our evaluation data. You will be responsible for ensuring our search models are being evaluated against comprehensive and high-quality datasets, across numerous domains. You’ll manage relationships with external vendors and make key strategic decisions on what data is important for our evals - and acquire it. Your work will play a key role in Exa’s mission of a perfect information society.

Who You Are

• Hustler in negotiating, securing, and managing relationships with external vendors
• Care and excellence in writing. At Exa we value human communications free of AI generated text
• High ownership and attention to detail
• Strong numerical literacy and technical background. CS/EE degree, production engineering experience, or equivalent depth earned on the job

What You Could Do

• Build specific, localized eval feedback and signal
• Write a manifesto of what perfect search means
• Define metrics and success criteria for our evaluation data suites
• Partner closely with ML researchers, data engineers, infra engineers, and product to shape the data loops that improve our search models

Logistics

• Location: This is an in-person opportunity in San Francisco.