CV
Current CV for Noah Flynn, Senior Research Scientist at Google Cloud AI.
Experience
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Apr 2026 – Present Senior Research Scientist
Google Cloud AI, Gemini Enterprise, Santa Clara, CA - Building agentic AI systems for Gemini Enterprise research, coding, and data science workflows.
- Developing evaluation, adaptation, and reliability methods for production AI assistants that reason over long context and use tools.
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Sep 2024 – Apr 2026 Applied Scientist II
Amazon AWS AI Labs, Agentic AI, Santa Clara, CA - Built customizable multi-agent primitives that shortened internal agent-development cycles for deep research, data science, database analysis, code transformation, and security-analysis workflows.
- Co-developed agent-as-a-judge evaluation methods for personalized assessment of deep research agents with internal AWS teams and enterprise customers.
- Engineered cost-aware reasoning and tool-use optimization for agents working with licensed, premium data sources.
- Mentored 5 applied scientists on agent systems, evaluation methodology, dynamic context optimization, and parameter-efficient reinforcement learning.
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Jun 2024 – Present Adjunct Faculty
UC Berkeley, Berkeley, CA - Teach CHEM 274B, a graduate course on software engineering, machine learning, and cheminformatics for molecular science.
- Mentor UC Berkeley MSSE capstone teams on applied machine learning and scientific software projects.
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Jun 2021 – Sep 2024 Research Scientist II
Amazon Alexa & AGI, Santa Clara, CA - Accelerated Alexa's expansion into 3 international markets by developing dynamic data selection methods that reduced multilingual NLU data requirements by 95%.
- Shipped and maintained 20 production releases of conversational AI models serving 100M+ monthly active users across 6 international markets.
- Contributed tool-use capabilities and API invocation strategies to the Amazon Nova foundation model family and Alexa+.
- Built calibration methods that improved robustness to uncertainty from upstream ASR transcription artifacts.
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May 2019 – Aug 2019 Machine Learning Intern
Merck, Kenilworth, NJ - Built generative models for de novo drug design integrated with docking pipelines.
- Explored reinforcement learning for targeted optimization of binding affinity and therapeutic property profiles.
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Aug 2017 – Jun 2021 PhD Candidate
Washington University in St. Louis, St. Louis, MO - Modeled drug metabolism and bioactivation networks with deep learning methods, including graph neural networks, to guide early-stage drug development.
- Developed and maintained XenoSite and XenoNet prediction web services for small molecule biochemistry.
- Developed signal detection methods for retrospective electronic health record analysis of drug-drug interactions and drug-induced liver injury.
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May 2016 – May 2017 Computational Research Fellow
NCSA, University of Illinois at Urbana-Champaign - Characterized proteomic and transcriptomic networks with graph-theoretic methods for engineered biological systems.
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Nov 2015 – May 2017 Software Engineer Intern
AbbVie, North Chicago, IL - Developed a bioinformatics visualization suite for drug-target interactions, protein-protein interactions, and regulatory networks.
Education
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Aug 2017 – May 2021 PhD, Deep Learning & Computational Biology
Washington University in St. Louis, St. Louis, MO - GPA 3.94/4.00
- Dissertation on graph neural networks for drug metabolism prediction.
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Aug 2013 – Jun 2017 BS, Bioengineering — Minor in Computer Science
University of Illinois at Urbana-Champaign, Champaign, IL - GPA 3.70/4.00