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 talented individuals specializing in mechanistic interpretability to develop and deploy scalable pipelines for analyzing and interpreting these models, helping us understand how the brain represents and processes information. The role combines rigorous engineering practices with cutting-edge research in model interpretability, working at the intersection of neuroscience and artificial intelligence.
Role & Responsibilities:
What we offer:
Application:
In addition to applying to the position, please send your CV and one-page interest statement to: recruiting@enigmaproject.ai
DESIRED QUALIFICATIONS:
Key qualifications:
Master's degree in Computer Science or related field with 2+ years of relevant industry experience, OR Bachelor's degree with 4+ years of relevant industry experience
Strong understanding of mechanistic interpretability techniques and research literature
Expertise in implementing and scaling ML analysis pipelines
Proficiency in Python and deep learning frameworks (i.e. PyTorch)
Experience with distributed computing and high-performance computing clusters
Strong software engineering practices including version control, testing, and documentation
Familiarity with visualization tools and techniques for high-dimensional data
Preferred qualifications:
Experience with feature visualization techniques (e.g., activation maximization, attribution methods)
Knowledge of geometric methods for analyzing neural population activity
Familiarity with circuit discovery techniques in neural networks
Experience with large-scale data processing frameworks
Background in neuroscience or computational neuroscience
Contributions to open-source ML or interpretability tools
Experience with ML experiment tracking platforms (W&B, MLflow)
EDUCATION & EXPERIENCE (REQUIRED):
Bachelor's degree and three years of relevant experience, or combination of education and relevant experience.
KNOWLEDGE, SKILLS AND ABILITIES (REQUIRED):
CERTIFICATIONS & LICENSES:
None
PHYSICAL REQUIREMENTS*:
WORKING CONDITIONS:
The expected pay range for this position is $126,810 to $151,461 annually.
Stanford University provides pay ranges representing its good faith estimate of what the university reasonably expects to pay for a position. The pay offered to a selected candidate will be determined based on factors such as (but not limited to) the scope and responsibilities of the position, the qualifications of the selected candidate, departmental budget availability, internal equity, geographic location and external market pay for comparable jobs.
At Stanford University, base pay represents only one aspect of the comprehensive rewards package. The Cardinal at Work website (https://cardinalatwork.stanford.edu/benefits-rewards) provides detailed information on Stanford’s extensive range of benefits and rewards offered to employees. Specifics about the rewards package for this position may be discussed during the hiring process.
Consistent with its obligations under the law, the University will provide reasonable accommodations to applicants and employees with disabilities. Applicants requiring reasonable accommodation for any part of the application or hiring process should contact Stanford University Human Resources by submitting a contact form.
Stanford 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.
Additional Information