Machine Learning Research Scientist

Machine Learning Research Scientist (1 Year Fixed Term) at Stanford University summary:

The Machine Learning Research Scientist role at Stanford University's Enigma Project focuses on designing and implementing large-scale multimodal deep learning models to understand brain function and intelligence. The position involves training and optimizing advanced AI models using extensive neural data, providing technical leadership, and collaborating across disciplines in computational neuroscience and AI. This fixed-term role offers a dynamic research environment with access to unique datasets and state-of-the-art infrastructure.

The Enigma Project (enigmaproject.ai) is a research organization based in the Department of Ophthalmology at Stanford University School of Medicine dedicated to understanding the computational principles of natural intelligence using the tools of artificial intelligence. Leveraging recent advances in neurotechnology and machine learning, this project aims to create a foundation model of the brain, capturing the relationship between perception, cognition, behavior, and the activity dynamics of the brain.

This ambitious initiative promises to offer unprecedented insights into the algorithms of the brain while serving as a key resource for aligning artificial intelligence models with human-like neural representations.

As part of this project, we seek exceptional individuals with extensive experience building, using, and fine-tuning large-scale multimodal foundation models. The team will be responsible for training frontier multi-modal models on large-scale data of neuronal recordings that relate sensory input to neuronal correlates of perception, action, cognition, and intelligence. We expect the candidate to have expertise in modern deep learning libraries (preferably PyTorch) and recent developments in multimodal foundation models.

This position promises a vibrant atmosphere at Stanford University in a collaborative community renowned for expertise in computational neuroscience and deep learning.

Role & Responsibilities:

• Design and implement large-scale multimodal deep learning architectures that relate sensory inputs to neuronal correlates of perception, action, and cognition
• Develop novel computational approaches for training and optimizing frontier models on unprecedented amounts of neural data
• Provide technical leadership in distributed training systems and model optimization techniques
• Guide cross-functional teams in establishing technical frameworks and evaluation metrics for brain foundation models
• Communicate research findings through publications, presentations, workshops and research blogs
• Stay ahead of the latest developments in machine learning and neuroscience, and propose innovative solutions to advance the project's goals
• * - Other duties may also be assigned

What we offer:

• A rich environment in which to pursue fundamental research questions in AI and neuroscience
• A dynamic team of engineers and scientists in a project dedicated to one mission, rooted in academia but inspired by science in industry.
• Access to unique datasets spanning artificial and biological neural networks
• State-of-the-art computing infrastructure
• Competitive salary and benefits package
• Collaborative environment at the intersection of multiple disciplines
• Location at Stanford University with access to its world-class research community
• Strong mentoring in career development

Application:
In addition to applying to the position, please send your CV and one page interest statement to:

** The job duties listed are typical examples of work performed by positions in this job classification and are not designed to contain or be interpreted as a comprehensive inventory for all duties, tasks, and responsibilities. Specific duties and responsibilities may vary depending on department or program needs without changing the general nature and scope of the job or level of responsibility.

Employees may also perform other duties as assigned.

Desired Qualifications:

• Ph.D. in Computer Science, Machine Learning, Computational Neuroscience, or related field plus 2+ years post-Ph.D. research experience
• At least 2+ years of practical experience in training, fine-tuning, and using multi-modal deep learning models
• Strong publication record in top-tier machine learning conferences and journals, particularly in areas related to multi-modal modeling
• Strong programming skills in Python and deep learning frameworks
• Demonstrated ability to lead research projects and mentor others
• Ability to work effectively in a collaborative, multidisciplinary environment

Preferred Qualifications:

• Background in theoretical neuroscience or computational neuroscience
• Experience in processing and analyzing large-scale, high-dimensional data of different sources
• Experience with cloud computing platforms (e.g., AWS, GCP, Azure) and their machine learning services
• Familiarity with big data and MLOps platforms (e.g. MLflow, Weights & Biases)
• Familiarity with training, fine tuning, and…