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Stanford University seeks software developers/engineers to support a multi-lab Neuro-AI project aimed at building a foundation model of the brain. You will help ensure reproducibility, production-readiness, and implement CI/CD across teams in a cutting-edge neuroscience environment.
You will work with Python and system-level languages (C/C++, Rust) and leverage Docker, Kubernetes, and Slurm for scalable workflows, while contributing to ML frameworks like TensorFlow or PyTorch.
The Department of Ophthalmology in the School of Medicine at 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 talented software developers/engineers to support the whole team by developing and scaling the systems that allow our scientists to iterate quickly. In this role, you will play a critical role in ensuring the reproducibility and production-readiness of our codebase, as well as implementing Continuous Integration and Continuous Deployment (CI/CD) processes for all teams involved in the project. Your work will be essential in maintaining the highest standards of software quality, reliability, and efficiency.
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.