Will was one of the first engineers at Cresta where he built real time AI products. He studied CS and physics at Harvard, where he researched human/AI interaction and led the robotics club. Will considers himself an expert in both embedding models and chocolate chip cookies -- the jury is still out on which is more critical for company operations.

Jeff spent three years building data and web infra at Plaid. He studied CS and Philosophy at Harvard, where he ran a GPU cluster in his dorm room and was roommates with Will. The team estimates that 20% of social analysis in San Francisco traces back to one of Jeff's many viral tweets.

Ben previously did quant trading at SIG and before that took the hardest math course in the country at Harvard. When we find frisbees, tailor made suits, or scribbled math formulas lying around the office, there's usually a Ben behind it.

Hubert previously worked on projects like particle simulations and automated wheelchairs. He studied CS in the Yao Class at Tsinghua University. Hubert's appetite for clean microservice architecture is perhaps only matched by his appetite for Haribo sour candy.

Shreyas previously worked on various projects, from training neural networks in Haskell to building a game streaming engine. He studied CS at the University of Waterloo. You can typically find Shreyas analyzing the price/performance of AWS services or crushing the team in basketball, sometimes at the same time.

Isabelle previously was at Mckinsey consulting for tech companies. She studied politics and philosophy at the University of Melbourne. Isabelle has the remarkable ability to work intensely on one screen while at the same time watching a technical lecture on another. The team is unsure whether this comes from McKinsey training or is just an Australian thing.

Eugene previously designed and built LLM products at Palantir. He studied CS at Minerva University. Eugene loves two things and hates one -- designing beautiful frontends, optimizing high performance backends, and eating vegetables.

Michael previously worked on ML and privacy at various companies, including Apple. He studied CS at Harvard University. He also somehow finds time to cook chef-level meals and have PhD-level knowledge on nearly everything -- both of which the team enjoys consuming.

Thais was previously VP of Marketing at Nate, Hubla and most recently co-founded her own startup in Brazil. She studied Economics and Econometrics at the University of Chicago. She might beat Exa's search in the amount of restaurant recommendations or Taylor Swift references she can provide on the spot.

Vishal was previously a management consultant at McKinsey & Co., and was part of the strategy team at TikTok Australia. He studied EECS and Finance at Monash University Australia. Vishal is the very rare person who can both code up any product and sell it to anyone. Given his messianic startup skills and obsession with Dune 2, some believe the similarity between 'Vishal' and 'Paul' is more than a coincidence.

Felix previously worked on opensource projects from next-gen text editors to composable knowledge management systems. He (almost) studied CS and philosophy at UIUC until he realized that he is already a beast. The only job Felix should not do is corrosion engineering, because he deeply wishes to convert the world into rust.

Stacey previously worked on customer success and business development at YC-backed Awesomic. She got a bachelors in German Philology and a masters in communications from Taras Shevchenko National University of Kyiv, basically the Harvard of Ukraine. Stacey goes by many names at Exa -- workplace operator, recruiting coordinator, chief happiness officer -- but perhaps her most beloved name is "greatest cookie baker of all time". These cookies are unfairly delicious.

You previously worked on some project that demonstrated exceptional skill. You studied CS at somewhere, but far more importantly want to learn by joining a startup working on massive-scale ML/infra. You are excited to tackle a mission as old as ancient greece -- organize the world's knowledge -- and recognize that to do that You must meet Us and become We.