Research Scientist

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 research on synthetic data scaling (BeyondWeb https://www.datologyai.com/blog/beyondweb) and pretraining with domain-specific data (The Finetuner’s Fallacy https://www.datologyai.com/blog/finetuners-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 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'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. WHAT YOU'LL WORK ON - The research literature is vast, rife with ambiguity, and constantly evolving. You'll source, vet, implement, and improve promising ideas from the literature and your own thinking. - Our research is guided by concrete customer needs and product outcomes, not conference benchmarks. You'll have clear context on why your work matters and who it serves. HOW YOU'LL WORK - We believe researchers do their best work with autonomy. You'll have the freedom to pursue problems in the way that works best for you, with the resources and context to back it up. - We expect Research Scientists to collaborate closely with engineers, talk to customers, and shape the product vision. ABOUT YOU - 3+ years of deep learning research experience - Strong fundamentals in deep learning - Practical experience and/or publications in one or more of the following areas: - Data pruning and curation - Curriculum learning - Synthetic data generation - Dataset distillation - Effects of training data on model behavior - Embedding models and semantic search - Training large vision (including video), language, or multimodal models - Efficient ML - Enough software engineering and PyTorch experience (or willingness to learn) to run large-scale experiments and build production prototypes - A demonstrated track record in deep learning research, whether through papers, tools, or other artifacts Nice to have: - Experience with distributed data processing tools like Spark or Snowflake - Experience building and shipping ML products Candidates do not need a PhD or extensive publications. Some of the best researchers we've worked with have no formal training in machine