About the role
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 Director to lead a team of researchers designing the datasets that define and advance the frontier of AI. This team sits at the intersection of research, product strategy, and go-to-market. You and
the team will collaborate with academic partners, work with researchers at frontier labs, and utilize internal tools for evaluation and error analysis to identify the data, skills and capabilities that will improve model performance. You will uncover gaps in current frontier models and design a wide variety of RL and domain specific benchmarks. MAIN
RESPONSIBILITIES Define the process and decide what data we build next by combining market signal from customers and GTM with your team’s read on where frontier labs are headed. Define and own the quality for benchmark design and support GTM in positioning it against existing datasets. Mentor and grow a technical team comfortable with x-functional and customer- facing work, setting a high standard for quality and velocity while staying hands-on yourself. Foster external and academic collaborations related to our areas of interest Stay at the frontier: Track new benchmarks, agentic evaluation, and RL training research and tie back to the dataset portfolio we focus on PREFERRED
