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.
The ML organization 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.
We're looking for an ML evals engineer to design and build our eval stack at Exa. The role involves investigating how to evaluate search engines in an LLM world and then building the most comprehensive, creative, and effective eval suite. You will be deciding the future of search through the evals we choose to optimize for - your work will directly influence what the research team works on and shape the direction of the company.
Who You AreHave hands-on ML experience (training, finetuning, or evaluating models (bonus if related to embeddings or LLMs)
Have strong engineering fundamentals and can build reliable systems (Python, Rust, distributed pipelines, GPU/cluster jobs, etc.)
Enjoy diving into data via building eval sets, inspecting edge cases, designing creative measurement strategies
Write a manifesto of what perfect search means
Design and implement evaluation frameworks that probe the limits of search
Build scalable, reliable eval pipelines that track regressions, drift, and quality signals across billions of documents
Create golden datasets, synthetic benchmarks, agentic tasks, and real-world test suites that reflect how developers, agents, and humans actually use Exa
Partner closely with ML researchers, data engineers, infra engineers, and product to shape the feedback loops that improve our search models
Location: This is an in-person opportunity in San Francisco.
Visas: We're happy to sponsor international candidates (e.g., STEM OPT, OPT, H1B, O1, E3). While we cannot guarantee your visa, we have historically been successful in sponsoring candidates from all over the world. If you receive an offer, our team will work hard to get you a visa.
Benefits: We offer premium healthcare benefits (medical, dental, vision), fertility benefits, 16 weeks of fully paid parental leave for all new parents, and a monthly wellness stipend to all of our employees.
Skills Required
- Some machine learning experience
- Strong engineering experience
- Experience creating evaluation datasets
- Passion for search problem
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.
