Director, Research - Evaluation & Training
Snorkel AI · San Francisco, CA (Hybrid)
About Snorkel At Snorkel, we believe meaningful AI doesn’t start with the model, it starts with the data. We’re on a mission to help enterprises transform expert knowledge into specialized AI at scale. The AI landscape has gone through incredible changes since 2015, when Snorkel started as a research project in the Stanford AI Lab, to the generative AI breakthroughs of today. But one thing has remained constant: the data you use to build AI is the key to achieving differentiation, high performance, and production-ready systems. We work with some of the world’s largest organizations to empower scientists, engineers, financial experts, product creators, journalists, and more to build custom AI with their data faster than ever before. Excited to help us redefine how AI is built? Apply to be the newest Snorkeler! ABOUT THE ROLE We're looking for a manager to lead a team of researchers to focus on data evaluation, error analysis and data valuation methods to predict model performance. This team is responsible for showcasing the value and quality of Snorkel’s data for model training and evaluation, understanding where today's frontier models fall short, and turning that understanding into a point of view on what benchmarks and datasets these models will benefit from. You and your team will be responsible for Snorkel’s data design flywheel by analyzing model failures, finding capability and skill gaps in current models, suggesting the next benchmarks to invest in and then proving the value of this data for our customers. MAIN RESPONSIBILITIES Own a multi-quarter roadmap centered on novel evaluation, error analysis, and data valuation techniques Synthesize and share trends from model-failure analysis and benchmarking into recommendations on the datasets the community should focus on and the ones Snorkel should invest in — making this team a primary input to the company's data strategy. Focus on data valuation techniques that quantify how Snork