Research Scientist - Interpretability (1 Year Fixed Term)

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 novel methods and scalable systems 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:

• 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
• 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
• * - Other duties may also be assigned

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
• Application:
  In addition to applying to the position, please send your CV and one page interest statement to: [email protected]
  **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 quantization of LLMs or multimodal models using common techniques and frameworks (LoRA, PEFT, AWQ, GPTQ, or similar)
• Experience with large-scale distributed model training frameworks (e.g. Ray, DeepSpeed, HF Accelerate, FSDP)

EDUCATION & EXPERIENCE (REQUIRED):
Bachelor's degree and five years of relevant experience, or combination of education and relevant experience.

KNOWLEDGE, SKILLS AND ABILITIES (REQUIRED):

• Expert knowledge of the principles of engineering and related natural sciences.
• Demonstrated project leadership experience.
• Demonstrated experience leading and/or managing technical professionals.

CERTIFICATIONS & LICENSES:
None

PHYSICAL REQUIREMENTS*:

• Frequently grasp lightly/fine manipulation, perform desk-based computer tasks, lift/carry/push/pull objects that weigh up to 10 pounds.
  Occasionally stand/walk, sit, twist/bend/stoop/squat, grasp forcefully.
• Rarely kneel/crawl, climb (ladders, scaffolds, or other), reach/work above shoulders, use a telephone, writing by hand, sort/file paperwork or parts, operate foot and/or hand controls, lift/carry/push/pull objects that weigh >40 pounds.
• * - Consistent with its obligations under the law, the University will provide reasonable accommodation to any employee with a disability who requires accommodation to perform the essential functions of his or her job.

WORKING CONDITIONS:

• May be exposed to high voltage electricity, radiation or electromagnetic fields, lasers, noise > 80dB TWA, Allergens/Biohazards/Chemicals /Asbestos, confined spaces, working at heights 10 feet, temperature extremes, heavy metals, unusual work hours or routine overtime and/or inclement weather.
• May require travel.

The expected pay range for this position is $156,560 to $180,039 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 ( ) 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.