Paige Bailey

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.