Stanford University
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.
Lead research initiatives in the mechanistic interpretability of foundation models of the brain
~ 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
~ Help shape the research agenda of the interpretability team
~* - An environment in which to pursue fundamental research questions in AI and neuroscience interpretability
Access to unique datasets spanning artificial and biological neural networks
Location at Stanford University with access to its world-class research community
Application:
ai **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. 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 programming skills in Python and deep learning frameworks Demonstrated ability to lead research projects and mentor others 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. 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. EDUCATION & EXPERIENCE (REQUIRED):
Bachelor's degree and five years of relevant experience, or combination of education and relevant experience. Expert knowledge of the principles of engineering and related natural sciences. Demonstrated project leadership experience. Frequently grasp lightly/fine manipulation, perform desk-based computer tasks, lift/carry/push/pull objects that weigh up to 10 pounds.
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 ~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.
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 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. The Cardinal at Work website ( provides detailed information on Stanford's extensive range of benefits and rewards offered to employees. 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. 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.
ai **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. 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 programming skills in Python and deep learning frameworks Demonstrated ability to lead research projects and mentor others 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. 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. EDUCATION & EXPERIENCE (REQUIRED):
Bachelor's degree and five years of relevant experience, or combination of education and relevant experience. Expert knowledge of the principles of engineering and related natural sciences. Demonstrated project leadership experience. Frequently grasp lightly/fine manipulation, perform desk-based computer tasks, lift/carry/push/pull objects that weigh up to 10 pounds.
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 ~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.
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 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. The Cardinal at Work website ( provides detailed information on Stanford's extensive range of benefits and rewards offered to employees. 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. 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.