Stanford University is launching an interdisciplinary Neuro-AI project dedicated to building a foundation model of the brain. This endeavor will involve multiple labs and faculty across the Stanford campus, including the Wu Tsai Neurosciences Institute, Stanford Bio-X, and the Human-Centered Artificial Intelligence Institute. Leveraging cutting-edge advances in electrophysiology and machine learning, this project aims to create a functional "digital twin" -- a model that captures both the activity dynamics of the brain at cellular resolution and the intelligent behavior it generates, including perception, motor planning, learning, reasoning, and problem-solving.

This ambitious initiative promises to offer unprecedented insights into the brain's algorithms of perception and cognition while serving as a key resource for aligning artificial intelligence models with human-like neural representations. As part of this project, we are seeking a talented senior data engineer with extensive experience in data infrastructure engineering to lead a team of engineers in building robust data pipelines. The team is responsible for designing, building, and operating the data pipeline infrastructure, which includes the entire flow of data from neurophysiological data acquisition to storage, processing, and preparation for large-scale training of machine learning-based foundation models. Ideal candidates will have practical experience in designing and scaling big data pipelines and proficiency with tools and frameworks such as Apache Spark, Airflow, DeltaLake, or similar technologies.

This position promises a vibrant and cooperative atmosphere within the laboratories of Andreas Tolias (https://toliaslab.org), Tirin Moore (https://www.moorelabstanford.com) and other labs at Stanford University renowned for their expertise in perception, cognition, pioneering neural recording techniques, computational neuroscience, machine learning, and Neuro-AI research.