Top Tips from Netflix, NVIDIA, and Meta on Large Language Models (LLMs)

Susan Zhang is a Research Engineer at Meta AI working on developing large language models. She has over 10 years of experience building software systems tackling a wide variety of domains at scale, ranging from quantum simulations at Los Alamos National Laboratory to reinforcement learning systems at OpenAI.

Faisal Siddiqi has built and leads the Machine Learning Platform org at Netflix, responsible for accelerating the impact of all ML practice including personalized recommendations, growth, studio, content, streaming, and systems applications. This ML Platform provides opinionated, flexible, and scalable solutions to help research scientists and engineers be productive with all stages of the ML lifecycle from early exploration through productization. Prior to joining Netflix in 2015, Faisal led the initial platform engineering team at Conviva, a streaming-media quality-of-experience startup. Early career work included network performance and distributed systems domains, as well as an entrepreneurial stint at a grad-school dorm startup. Faisal has an MSEE from Stanford University. Originally from India, Faisal has called the SF Bay Area home since 1998. In his free time, he likes to paint oil landscapes, but stands no chance against DALL-E-2, Midjourney and their ilk.

Bryan Catanzaro is Vice President of Applied Deep Learning Research at NVIDIA, where he leads a team of AI researchers working on chip design, audio and speech, language modeling, graphics and vision, with the goal of finding practical new ways to use AI for NVIDIA’s products and workflows. DLSS, Megatron, CUDNN, Pascaline, WaveGlow and DeepSpeech are some of the projects he’s helped create. Bryan received his PhD in EECS from the University of California, Berkeley.

Erhan Bas is currently working as a ML engineer at ScaleAI. Before joining Scale, he was with the AWS-AI Computer Vision group as a tech lead working on multimodal learning and biomedical CV. Before joining AWS, he worked as a computer scientist at HHMI, where he built large scale computational pipelines for bioscience applications. He received his B.Sc in Electrical and Electronics Engineering and in Physics in 2005 from the Middle East Technical University, Turkey. He obtained his M.Sc. in Electrical and Computer Engineering from Koc University, Turkey in 2007 and his Ph.D. in Electrical Engineering from Northeastern University, Boston in 2011. His interests include machine learning and computer vision with various applications in multimodal search and bioscience applications. He served as a member of IEEE Bio Imaging and Signal Processing Technical Committee and is a member of IEEE, EMBS, and HKN Eta Kappa Nu.

Elliot Branson Is the Director of Machine Learning and Engineering at Scale and leads the Machine Learning, Platform, Federal, 3D, and Mapping products. In his prior work, he helped create the Cruise Automation self-driving car and served as the first Head of Perception and AI. His interest in robotics and AI started with national and international robotics competitions in high school and continued in college and grad school where he published work on field robotics, localization, computer vision, and AI systems. His previous work includes stints on the Google Project Tango AR platform and Air Force MURI research programs.