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ML Technical Leader & Applied Researcher | RecSys, Generative AI, & Preference Alignment | Spotify, Netflix, LinkedIn, LLNL
I work at the intersection of large-scale Recommender Systems and frontier generative AI. Over the last 15+ years, I’ve had the opportunity to help build and scale personalization ecosystems at Spotify, Netflix, and LinkedIn. My recent work centers on the industry's architectural shift away from legacy, embedding-centric frameworks toward generative recommenders and model-heavy LLMs. I focus on both designing these new architectures and guiding the cross-functional engineering organizations required to make this paradigm shift a reality. Recently, this has involved steering broad efforts to evaluate and adapt open-weight LLMs for recommendation, and helping shape the strategy and tactics for how this pivot scales across a massive technical ecosystem. Coming from a background as a Ph.D. scientist and Principal Investigator at Lawrence Livermore National Laboratory, I work to bring a rigorous, hypothesis-driven approach to engineering. Ultimately, my goal is to bridge foundational science with pragmatic deployment, helping teams build novel, personalized experiences for hundreds of millions of users.
Duke University
Ph.D., Computational Biology and Bioinformatics
January 1, 2004 – January 1, 2010
University of South Florida
BS, Computer Science, Mathematics
January 1, 1997 – January 1, 2001
Spotify
Machine Learning Technical Lead
July 1, 2022 – November 1, 2025
San Francisco Bay Area · Remote
Netflix
Machine Learning Research Scientist
May 1, 2019 – June 1, 2022
Los Gatos, CA
Machine Learning Engineer
October 1, 2016 – May 1, 2019
Mountain View, CA
Lawrence Livermore National Laboratory
Machine Learning Group Leader, Researcher, Principal Investigator
February 1, 2012 – October 1, 2016
Livermore, California
Lawrence Livermore National Laboratory
Postdoctoral Researcher
August 1, 2010 – January 1, 2012
Livermore, California
Duke University
Graduate Student
January 1, 2004 – September 1, 2010
Lockheed Martin Information Systems Advanced Simulation Center
Associate Software Engineer
October 1, 2002 – July 1, 2004
GMx Technologies
Software Engineer
June 1, 2002 – October 1, 2002
Networked Knowledge Systems
Software Engineer
February 1, 2002 – June 1, 2002
Cultural Fit Analysis
The candidate's career trajectory shows a strong inclination towards advanced research and development in Machine Learning and AI, working in prominent tech companies and national laboratories. While the target role is 'Data Analyst', their background is heavily skewed towards Machine Learning Engineering and Research, which might indicate an overqualification or a potential mismatch in day-to-day responsibilities if the Data Analyst role is purely focused on descriptive analytics and reporting. However, their diverse project experience, from computational biology to cybersecurity and predictive medicine, demonstrates adaptability and a broad interest in applying data science principles across different domains. The transition from a highly specialized ML role to a Data Analyst role would require understanding the candidate's motivation and expectations.
Soft Skills & Operational Fit
The candidate's experience as a Machine Learning Technical Lead and Group Leader at Spotify and Lawrence Livermore National Laboratory indicates strong leadership, mentorship, and cross-functional collaboration skills. Their work on guiding AI strategy and aligning algorithms with product goals suggests a strategic mindset and ability to translate complex technical concepts into business value. The descriptions also imply strong problem-solving and research capabilities.