Datology automatically curates model training data. Train faster, achieve better performance, and reduce costs with enterprise-grade data curation. Backed by Felicis and Amplify.
About the role
In this role, you'll be our expert on social, the person who knows how to make DatologyAI's research travel on X and across the AI community. You'll turn our research into narratives, demos, threads, videos, and launches, build a voice on X that people recognize and trust, and take part in the conversation as it happens. You'll also help our founders and researchers communicate in their own voices.
More about this role
Models are what they eat. But a large portion of training compute is wasted training on data that are already learned, irrelevant, or even harmful, leading to worse models that cost more to train and deploy.
At DatologyAI, we’ve built a state of the art data curation suite to automatically curate and optimize petabytes of data to create the best possible training data for your models. Training on curated data can dramatically reduce training time and cost ( 7-40x faster training depending on the use case), dramatically increase model performance as if you had trained on >10x more raw data without increasing the cost of training, and allow smaller models with fewer than half the parameters to outperform larger models despite using far less compute at inference time, substantially reducing the cost of deployment. For more details, check out our recent research on synthetic data scaling ( BeyondWeb ) and pretraining with domain-specific data ( The Finetuner’s Fallacy ).
We raised a total of $57.5M in two rounds, a Seed and Series A. Our investors include Felicis Ventures, Radical Ventures, Amplify Partners, Microsoft, Amazon, and AI visionaries like Geoff Hinton, Yann LeCun, Jeff...
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