Research Scientist

About the 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 blog posts sharing our high-level results for text models and image-text models . 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 Dean, and many others who deeply understand the importance and difficulty of identifying and optimizing the best possible training data for models. Our team has pioneered this frontier research area and has the deep expertise on both data research and data engineering necessary to solve this incredibly challenging problem and make data curation easy for anyone who wants to train their own model on their own data. This role is based in Redwood City, CA. We are in office 4 days a week. We're looking for a Research Scientist to investigate how intervening on training data can improve the quality and shape the behavior of deep learning models. You'll source and implement ideas from the literature, conduct research grounded in real customer needs, and collaborate closely with engineers and product teams to turn findings into tangible impact. This role requires strong scientific judgment, fluency with the deep learning literature, and the drive to work autonomously in a fast-moving startup environment.