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Technical leader in AI. Vision, Generative Media, Personalization, Multimodal, Predictive Modeling, LLMs. Disney; Ex-Google, Amazon, NASA. PhD, MS, ME
Offering expertise and extensive technical leadership experience in artificial intelligence in end-to-end AI applied machine learning, research, and production; and a passion for product impact and the customer experience. Working at Google to advance the frontier of multimodal generative AI. Specialist in multimodal generative AI (vision, video, mixed media, and more); computer vision, machine learning, natural language processing, and AI. Specializing in zero-to-one AI-based product incubation- from ideation to prototyping to launching a first products and features. Working at Google in partnerships with Deepmind, in Labs, the GenAI product incubation org known for NotebookLM, Flow (Veo), Whisk (Imagen), and more. Designing, implementing, training, hardening, and launching complex multi-modal generative AI systems, including with Gemini, Imagen, Veo, and other generative SOTA AI models. Formerly working as Senior Applied Scientist at Amazon, leading multiple ML/AI feature development projects for the Prime Video and Amazon Photos team, with models in production at a scale serving multi-millions of customers and inferencing on multi-billions of assets. Work focus includes video, image, and language processing, semi-supervised and self-supervised deep learning, ML system architectures, deep learning scalability, and more. Experienced leading exploration and bringing clarity to deeply ambiguous, open-ended algorithmic application spaces. Responsibilities include assessing and formulating technical direction and machine learning architecture and executing on technical vision to bring value to a wide customer base through computer vision and machine learning features. Formerly Machine Learning Senior Applied Scientist at Adobe working as technical lead to a newly formed ML team of five ML engineers. Responsible for setting technical direction, mentoring and growi
University of Virginia
Doctor of Philosophy (PhD), Mathematics with concentration Applied Mathematics
January 1, 2012 – January 1, 2014
University of Virginia
Master of Engineering (ME), Mechanical and Aerospace Engineering
January 1, 2009 – January 1, 2012
University of Virginia
Master of Science (M.S.), Mathematics (May 2009), concentration Applied Mathematics
January 1, 2007 – January 1, 2009
University of Chicago
Bachelor's degree, Mathematics
January 1, 2004 – January 1, 2007
Disney Entertainment
Principal Machine Learning Engineer
November 1, 2025 – Present
Seattle, Washington, United States · On-site
Staff Machine Learning Engineer
September 1, 2023 – November 1, 2025
Seattle, Washington, United States
Amazon
Sr. Applied Scientist, Prime Video
December 1, 2021 – September 1, 2023
Seattle, Washington, United States
Amazon
Senior Applied Scientist, Amazon Photos
January 1, 2020 – December 1, 2021
Seattle, Washington, United States
Adobe
Senior Applied Scientist: Machine Learning
August 1, 2018 – January 1, 2020
Seattle, Washington, United States
Boeing
Research and Technology Applied Mathematician: Scientific Computing and Artificial Intelligence
October 1, 2014 – August 1, 2018
Huntsville, Alabama, United States
NASA Glenn Research Center
NASA 2012 Aeronautics Academy Research Associate
June 1, 2012 – August 1, 2012
Cleveland, Ohio, United States
Johns Hopkins University Applied Physics Laboratory
Research Intern
June 1, 2011 – August 1, 2011
Laurel, Maryland, United States
University of Virginia
PhD, MS, ME Graduate Student
June 1, 2007 – August 1, 2014
Charlottesville, Virginia, United States
Certificate of Participation: MIT Summer School on Cognitive Robotics
Massachusetts Institute of Technology
June 24, 2026 – Present
Architecture and Systems Engineering: Models and Methods to Manage Complex Systems
Massachusetts Institute of Technology
June 24, 2026 – Present
Cultural Fit Analysis
The candidate demonstrates a strong cultural fit for an ML Engineer role, particularly in an innovative and fast-paced environment. Their experience spans multiple large tech companies, indicating adaptability and ability to thrive in diverse corporate cultures. The breadth of projects, from generative AI at Google to ad platform optimization at Disney, shows a versatile and curious mindset. The academic background in mathematics and engineering, combined with practical application in AI/ML, suggests a strong problem-solving orientation and a continuous learning approach.
Soft Skills & Operational Fit
The candidate's resume highlights leadership, mentorship, and project ownership, indicating strong operational fit for senior roles. Experience in setting organizational standards and leading multi-team projects suggests excellent collaboration and strategic thinking. The detailed descriptions of responsibilities imply strong communication skills in a professional context.