Research Scientist - Interpretability (1 Year Fixed Term)

Stanford University School of Medicine; Stanford (CA); On-site; USD 100,000 - 130,000; Full time

Benefits offered by this job

Competitive salary

Access to unique datasets

State-of-the-art computing infrastructure

Job summary

A leading medical research institution is seeking a Research Scientist in Interpretability to lead initiatives at the intersection of AI and neuroscience. The ideal candidate will have a Ph.D. and experience in deep learning and will be responsible for guiding research on neural representations and mentoring junior staff. This role offers a collaborative environment and access to cutting-edge resources at Stanford University.

Qualifications

• Ph.D. with 2+ years post-Ph.D. research experience.
• Strong publication record in top-tier machine learning conferences.
• Demonstrated ability to mentor junior researchers.

Responsibilities

• Lead research initiatives on the interpretability of models.
• Develop theoretical frameworks for neural representations.
• Mentor junior researchers in interpretability methods.

Skills

Deep learning

Python programming

Research leadership

Multi-modal deep learning models

Education

Ph.D. in Computer Science or related field

Tools

TensorFlow

PyTorch

Job description

Research Scientist - Interpretability (1 Year Fixed Term)

Join the Research Scientist - Interpretability (1 Year Fixed Term) role at Stanford University School of Medicine.

The Enigma Project 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.

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. The role combines rigorous engineering practices with cutting-edge research in model interpretability, working at the intersection of neuroscience and artificial intelligence.

Role & Responsibilities

• Lead research initiatives in the mechanistic interpretability of foundation models of the brain
• Develop novel theoretical frameworks and methods for understanding neural representations
• Design and guide interpretability studies that bridge artificial and biological neural networks
• Apply advanced techniques for circuit discovery, feature visualization, and geometric analysis of high-dimensional neural data
• Collaborate with neuroscientists to connect interpretability findings with biological principles
• Mentor junior researchers and engineers in interpretability methods
• Help shape the research agenda of the interpretability team

What We Offer

• An environment in which to pursue fundamental research questions in AI and neuroscience interpretability
• 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

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

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 and their machine learning services
• Familiarity with big data and MLOps platforms

Stanford University is an equal employment opportunity and affirmative action employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, protected veteran status, or any other characteristic protected by law.