Paige Bailey is an engineering lead for developer relations at Google DeepMind and a contributor to the research behind PaLM 2, Gemini, and Gemma. Her career spans geophysics, scientific Python, machine-learning infrastructure, developer tools, and frontier models, with a consistent emphasis on making advanced capabilities accessible to working developers.
Bailey began working with machine learning around 2009, contributing to open-source scientific-computing projects including NumPy, SciPy, Matplotlib, and scikit-learn. She studied geophysics and applied mathematics, pursued graduate work in computer science and carbonate geology, and worked at Chevron on subsurface geoscience, velocity modeling, drilling optimization, distributed computing, and GPU-based workloads.
She contributed to TensorFlow, worked on machine-learning developer experiences at Microsoft and Google, and joined GitHub, where her work included VS Code and early GitHub Copilot user-experience testing. Returning to Google, she contributed to the PaLM 2, Gemini, and Gemma model programs before leading developer-relations engineering at Google DeepMind.
Her independent projects include thinking-in-data, a VS Code extension pack for exploring and visualizing data; signals-and-systems, interactive visualizations for an open-source engineering textbook; and Gemini and Gemma examples, practical notebooks for contemporary AI models.
Open models as practical independence. Bailey advocates models developers can download, adapt, and run on their own infrastructure, including offline or privacy-sensitive environments. Her characteristically irreverent case for open models emphasizes affordability, stability, and freedom from unexpected platform changes.
Model selection as an engineering decision. She evaluates models against cost, latency, tools, and actual task performance. Her hands-on Gemini demonstration shows a smaller model using sandboxed Python to analyze images quickly; her assessment of computer-use benchmarks for CAD argues that affordable systems approaching frontier performance could broaden access to physical-design tools.
Multimodal applications with persistent state. Bailey turns model capabilities into complete software: one example identifies books from a photograph, supplements missing details through search, authenticates users, and stores their libraries in Firebase. She also distinguishes Project Genie’s dynamically generated video environments from exportable three-dimensional game assets.
Generative media with creative control and safeguards. Her work with Veo addresses camera control, visual consistency, synchronized dialogue, and SynthID watermarking. A commercial-reconstruction example contrasts manually stitching together generated video, speech, and music with a more integrated Veo 3 workflow.
Embodied AI with separated control layers. In robotics, Bailey distinguishes multimodal perception and high-level planning from specialized software executing physical movement locally—a practical architecture for applying advanced models to hardware without confusing reasoning with motor control.
From timestamped dinosaur videos to a persistent bookshelf catalog, Paige Bailey demonstrates how models, grounding, executable tools and app infrastructure work together.
Follow video analysis, a bookshelf app, a book-to-media pipeline, and local Gemma agents through working results, failed assumptions, and live repairs.
A tour of reference images, scene editing, native audio and music tools leads to a practical comparison: rebuilding a commercial with separate generators versus one Veo 3 prompt.
