Exa is building the search engine for the age of AI — from the silicon up. We run one of the most ambitious indexing operations in the world: crawling the open web at massive scale, training state-of-the-art embedding models to understand it, and powering everything through our own high-performance Rust-based vector database and a $5M H200 GPU cluster that regularly lights up tens of thousands of machines.
The ML organization sits at the heart of this 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.
Desired Experience
Have 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
Example Projects
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
This is an in-person opportunity in San Francisco. We're happy to sponsor international candidates (e.g., STEM OPT, OPT, H1B, O1, E3).
Top Skills
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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