Benjamin is an R&D ML Engineer, currently working at Answer.AI. His interests are focused on NLP and, especially, Information Retrieval. He has been involved in multiple projects with a focus on broadening the access to state-of-the-art retrieval methods, such as RAGatouille and the rerankers library. Recently, Ben also co-led the ModernBERT project, which has modernized the widely used BERT backbone. Previously, Ben has worked at legaltech companies LexisNexis and Jus Mundi, and he’s currently interested in exploring the new possibilities that LLMs open up for retrieval.

Hamel is a machine learning engineer with over 20 years of experience. He has worked with innovative companies such as Airbnb and GitHub, which included early LLM research used by OpenAI, for code understanding. He has also led and contributed to numerous popular open-source machine-learning tools. Hamel is currently an independent consultant helping companies build AI products.

Shreya is an experienced ML Engineer who is currently a PhD candidate in computer science at UC Berkeley, where she builds systems that help people use AI to work with data effectively. Her research focuses on developing practical tools and frameworks for building reliable ML systems, with recent groundbreaking work on LLM evaluation and data quality. She has published influential papers on evaluating and aligning LLM systems, including "Who Validates the Validators?" which explores how to systematically align LLM evaluations with human preferences.

Prior to her PhD, Shreya worked as an ML engineer in industry and completed her BS and MS in computer science at Stanford. Her work appears in top data management and HCI venues including SIGMOD, VLDB, and UIST. She is currently supported by the NDSEG Fellowship and has collaborated extensively with major tech companies and startups to deploy her research in production environments. Her recent projects like DocETL and SPADE demonstrate her ability to bridge theoretical frameworks with practical implementations that help developers build more reliable AI systems.