Machine Learning Systems Engineer

Machine Learning Systems Engineer (1 Year Fixed Term) at Stanford University summary:

The Machine Learning Systems Engineer role at Stanford University's Department of Ophthalmology involves designing, deploying, and maintaining large-scale compute infrastructure supporting advanced Neuro-AI research. This interdisciplinary project integrates neuroscience, machine learning, and engineering to build a digital twin model of the brain, collaborating with multiple renowned labs. The position requires expertise in HPC, cloud computing, containerization, and scripting within a collaborative academic environment.

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 systems engineers with extensive experience in large scale data and compute clusters. As a Systems Engineer, you will be responsible for designing, deploying, and maintaining the compute infrastructure that supports our machine learning and data pipeline operations.

This position promises a vibrant and cooperative atmosphere within the laboratories of Andreas Tolias ( ), Tirin Moore ( ) 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.

Duties include:

• · Design and develop complex and specialized equipment, instruments, or systems; coordinate detailed phases of work related to responsibility for part of a major project or for an entire project of moderate scope.
• · Develop technical and methodological solutions to complex engineering/scientific problems requiring independent analytical thinking and advanced knowledge.
• · Develop creative new or improved equipment, materials, technologies, processes, methods, or software important to the advancement of the field.
• · Contribute technical expertise, and perform basic research and development in support of programs/projects; act as advisor/consultant in area of specialty.
• · Contribute to portions of published articles or presentations; prepare and write reports; draft and prepare scientific papers.
• · Provide technical direction to other research staff, engineering associates, technicians, and/or students, as needed.
• * - Other duties may also be assigned

What we offer:

• · Work on a collaborative and uniquely positioned project spanning several disciplines, from neuroscience to artificial intelligence and engineering.
• · Work jointly with a vibrant team of researchers and scientists in a project dedicated to one mission, rooted in academia but inspired by science in industry.
• · Competitive salary and benefits.
• · Strong mentoring in career development.

Application:

In addition to completing the application, please send your CV and one page interest statement to:

DESIRED

QUALIFICATIONS:

• · 3+ years of experience in designing, managing and running large-scale compute infrastructure in the context of machine learning
• · Experience with containerization technologies like Docker and orchestration platforms like Kubernetes or SLURM
• · Proficiency in scripting languages such as Python, Bash, or Power Shell
• · Strong knowledge of Linux/Unix systems administration
• · Ability to work effectively in a collaborative, multidisciplinary…