Greg Papierniak
Danville, California,United States
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Greg Papierniak is a distinguished Staff Software Engineer at Google AI, specializing in the development and scaling of large-scale machine learning systems. With a strong background in computer science and artificial intelligence, his work primarily focuses on building foundational models and the infrastructure required to train and serve them effectively. He has made significant contributions to some of Google's most ambitious AI projects, including advancements in natural language processing and multimodal AI. Greg is passionate about pushing the boundaries of AI capabilities, tackling complex engineering challenges to make sophisticated models more efficient, robust, and accessible. His expertise lies at the intersection of cutting-edge research and practical, high-performance system implementation.
Professional Summary
Greg Papierniak's work history includes a series of influential roles in various companies. Here is a detailed list of his professional journey:
Notable Achievements
Key Contributor to Pathways Language Model (PaLM) & PaLM 2
Played a pivotal role in the engineering and development of Google's Pathways Language Model (PaLM) and its successor PaLM 2, some of the largest and most advanced language models, contributing to their architecture, training efficiency, and scaling.
Advancements in Large-Scale AI/ML Infrastructure
Led and contributed to the design and implementation of highly scalable and reliable infrastructure for training and deploying state-of-the-art machine learning models at Google, enabling groundbreaking AI research and product integration.
Development of Efficient AI Systems
Instrumental in optimizing machine learning workflows and systems, focusing on improving performance, reducing latency, and enhancing the overall efficiency of AI model development and deployment cycles.
Work on Multimodal AI Models (e.g., Gemini)
Contributed to the engineering efforts behind Google's multimodal AI models like Gemini, which can understand and operate across different types of information like text, code, images, and video.
Educational Background
Political Science, Political Theory and International Relations
University of California, Davis - Year 2006
San Ramon Valley High School
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Company Overview
AppsFlyerAppsFlyer is a SaaS mobile marketing analytics and attribution platform. It helps app marketers, brands, and agencies make better marketing decisions by providing comprehensive measurement and analytics tools. Users can track and optimize their ad spend across various channels, understand user acquisition funnels, measure campaign performance, and combat mobile ad fraud, ultimately aiming to improve ROI and user engagement for their mobile applications.
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