Daniel Marcu

Daniel Marcu

Partner and Global Head of AI Engineering and Science

Goldman Sachs

Daniel is global head of AI Engineering and Science. He joined the firm as a partner in 2025.

Prior to joining the firm, Daniel worked at Amazon, where he spent eight years and most recently served as vice president of Web and Knowledge Services in Alexa Information and then Amazon Artificial General Intelligence. Before that, he spent more than 20 years at the Information Sciences Institute and University of Southern California, where he served as director of Strategic Initiatives. Earlier in his career, Daniel served as chief science officer of SDL plc, and chief technology and operations officer and chief executive officer of Language Weaver Inc, a machine translation company that he co-founded.

Daniel has authored an MIT Press book and more than 100 peer-reviewed scientific publications. He holds more than 35 patents with the US Patent and Trademark Office and was named an Association for Computational Linguistics Fellow for his significant contributions to discourse parsing, summarization and machine translation, as well as for kickstarting the statistical machine translation industry.

Daniel earned a BSc in Automation and Computers from Technical University of Cluj, Romania, an MSc and PhD in Computer Science from the University of Toronto, and an executive MBA from UCLA’s Anderson School of Management.

Past Sessions

Wednesday, March 25, 2026
12:45 pm

Generative AI has moved beyond experimentation into production at scale—Goldman Sachs alone has deployed AI tools to thousands of employees and is working with partners to build autonomous agents for accounting and client onboarding. This fireside chat examines how various frameworks must evolve when actions occur at machine speed. Daniel Marcu, Partner and Global Head of AI Engineering and Science at Goldman Sachs, will discuss:

  • How Goldman Sachs has introduced generative AI to its workforce—and what has changed as more agentic frameworks take place
  • Building trust into AI systems from the ground up: explainability, auditability, and control
  • The path forward: which banking functions are ready for autonomy today, and what still requires human judgment