Research Engineer

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. As a Research Engineer, you will play a crucial role in conducting and enabling cutting-edge research and translating it into our core product pipeline. You will work closely with other members of the technical staff to develop and improve state-of-the-art data curation strategies. Your technical skills will accelerate our research and ensure that our product remains at the forefront of innovation. About You Bachelor's degree or equivalent practical experience 4+ years of experience in an industry research lab or equivalent academic experience Strong background in machine learning systems, including distributed training of large models and ML performance optimization Deep understanding of ML and AI models, particularly foundation models, how they are built, trained, and used Strong foundations in software engineering and empirical research Experience contributing to the research community through open-source projects and or publications at top-tier conferences such as CVPR, NeurIPS, ICCV or ECCV, BMVC Experience working across the research-to-product pipeline in industry or academic research labs Ability to work independently and collaboratively with excellent communication and presentation skills PhD in Computer Science, Machine Learning, or a related technical field preferred