We're looking for an ML research engineer to train embedding models for perfect search over the web. The role involves dreaming up novel transformer-based search architectures, creating datasets, creating evals, beating our internal SOTA, and repeat!
We're an SF team of ~20 engineers/researchers from Harvard, MIT, Apple, etc. We recently raised a $17m Series A from Lightspeed and Nvidia, and we just bought a $5m H200 cluster to train more models.
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At Exa, we're training 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.
No other search engine can -- or is even trying -- to do this (SearchGPT, Google, Perplexity are innovating on the UI, not the search algorithm). And no other AI lab we know of is exploring this direction at web-scale.
That means if we don't find novel methods for search, they won't be found. Our team has already made breakthroughs not seen in the literature, and we have more coming. Want to explore the search frontier with us?
Desired Experience
- You have graduate-level ML experience (or are an exceptionally strong undergrad)
- You can code up a transformer from scratch in pytorch
- You're comfortable creating large-scale datasets
- Care about the problem of finding high quality knowledge and recognize how important this is for the world
Example Projects
- Train a 70B parameter version of our current model
- Build an RLAIF pipeline for search
- Dream up a novel architecture for search in the shower, then code it up and beat our best model's top score
We are open to sponsoring International candidates (e.g STEM OPT, OPT, H1B, O1, E3)
This is an in-person opportunity in San Francisco. We're big believers in in-person culture!
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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.
