
DatologyAI builds tools to automatically select the best data on which to train deep learning models. Our tools leverage cutting-edge research—much of which we perform ourselves—to identify…

DatologyAI builds tools to automatically select the best data on which to train deep learning models. Our tools leverage cutting-edge research—much of which we perform ourselves—to identify…
What they do: Automated, modality-agnostic data curation tools to improve training efficiency, model performance, and reduce compute
Headcount (approx.): 51 employees
Funding: Approximately $57.5M total (Seed + $46M Series A)
Founding team: Co-founders include Ari Morcos (CEO), Bogdan Gaza (CTO), Matthew Leavitt (CSO) and others
Data curation for deep learning model training
Technology, Information and Internet
$11.65M
Seed included prominent angel investors and AI industry figures
$46M
Series A led by Viv Faga and Astasia Myers from Felicis Ventures
“Includes participation from high-profile AI figures (angel investors) and established VC firms”
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About The Company 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.
About The Role We are looking for seasoned Full-Stack engineers who love building new products in an iterative and fast-moving environment. In this role, you will build software from the ground up to solve critical bottlenecks for DatologyAI customers and internally. As one of our key hires, you will partner closely with our founders on the direction of our product and drive business-critical technical decisions.
You will contribute to developing the core product that customers use for curating their datasets and the visualizations around it, as well as the internal tooling that our team uses daily to develop the core product. You will have a broad impact on the technology, product, and our company's culture.
As a Software Engineer - Full-Stack at DatologyAI, you will be responsible for:
About You
Compensation At DatologyAI, we are dedicated to rewarding talent with highly competitive salary and significant equity. The salary for this position ranges from $180,000 to $300,000.
We offer a comprehensive benefits package to support our employees' well-being and professional growth:
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