So, geniuses in a datacenter do need to learn from experience in a physical lab. Sounds familiar
Genius is 99% perspiration, and 1% inspiration. This is also true for how we trained Periodic Neon to beat frontier models, like Astra and Fable 5.1, on a scientific capability our labs care about.
We trained Neon with reinforcement learning on our lab data using only 1.3K GPUs, orders of magnitude less than Astra. As we scale our labs and compute, we expect our models to tackle much more complex scientific problems.
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At Periodic, we love people with both science + LLM experience but would be very happy with folks who have:
- strong science backgrounds
- deep interest in AI tools
- a desire to learn the rest (LLMs, evals etc)
coding is getting easier, but doing science is not. we want to meet people with depth, accomplishments, and taste in science. if you have friends like this who want to work closer to AI, please send them our way.
Also you get to work with people like Dima or yours truly
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