MANAGEMENT   |   PARTNERS   |   ADVISORS

MATTHEW LEE
Artificial Intelligence

 

Matthew Lee is an AI and technology operator with experience building and scaling large scale artificial intelligence systems, data ecosystems, and operational infrastructure across some of the world’s most influential technology organizations. He currently serves as Senior AI Project Manager at Meta within Meta Superintelligence Labs, where he leads strategic initiatives supporting frontier AI research, advanced model development, and next generation AI operational systems.

At Meta Superintelligence Labs, Matthew oversees the strategy, planning, and execution of large scale data driven AI initiatives across research teams working on cutting edge foundation models and emerging intelligent systems. He has built and scaled global networks of elite researchers, domain experts, and human intelligence contributors responsible for training, evaluating, and refining advanced AI models. His work spans AI infrastructure, operational scaling, evaluation systems, agentic workflows, and human in the loop intelligence architectures designed to improve the performance, reliability, and capabilities of next generation AI platforms.

Prior to Meta, Matthew served as Strategic Projects and Growth Lead at Scale AI, where he led international operations supporting one of the industry’s largest generative AI and large language model deployments. He built and managed a global workforce of more than 3,000 contributors across over 30 language locales and developed adaptive AI powered evaluation systems to assess coding, reasoning, and technical performance across multiple disciplines. His work directly supported the scaling of advanced generative AI systems, large scale human data operations, and global AI infrastructure.

Earlier in his career, Matthew worked as a Data and Machine Learning Engineer at Amazon Web Services (AWS), where he designed and deployed enterprise scale machine learning, analytics, and data engineering systems for major international organizations. His work included building secure ETL pipelines using SQL and PySpark for global clients, leading engineering teams responsible for AWS NFL statistical overlays viewed by hundreds of thousands of users on Twitch.tv, and contributing to machine learning forecasting and cloud infrastructure initiatives within AWS consulting environments. He also published technical documentation, mentored engineers, and helped support strategic enterprise deployments across multiple industries.

Matthew’s background additionally includes public service at The White House within the Office of Presidential Advance, where he supported high profile Presidential operations involving complex logistics, stakeholder coordination, security planning, and rapid execution in mission critical environments. His responsibilities included coordinating Presidential travel operations, managing event logistics, collaborating with local governments and the United States Secret Service, and overseeing operational planning for major Presidential events nationwide.

In addition to his professional work, Matthew has demonstrated a strong passion for applied technology and innovation through independent engineering projects, including the development of a real time American Sign Language robotic translation system leveraging AWS technologies, robotics, and machine learning infrastructure. The project was later showcased at AWS re:Invent before hundreds of enterprise clients and technology leaders.

Matthew graduated from the University of California, Berkeley with a Bachelor of Arts in Data Science, where he focused on machine learning, data analytics, computational modeling, statistics, and large scale systems. Combining deep technical fluency with operational leadership experience across hyperscale AI environments, Matthew brings a unique perspective spanning frontier AI research, cloud infrastructure, human intelligence systems, and the future of intelligent technology platforms.