Machine Learning Research Scientist (1 Year Fixed Term)

Machine Learning Research Scientist (1 Year Fixed Term)

School of Medicine, Stanford, California, United States

Research

Post Date Jul 16, 2025

Requisition # 106890

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 : recruiting@enigmaproject.ai

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.

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 acontact 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

Schedule : Full-time

Job Code : 4982

Employee Status : Regular

Grade : K

Requisition ID : 106890

Work Arrangement : On Site