Research Scientist - Interpretability (1 Year FixedTerm)

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 : recruiting@enigmaproject.ai

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 : Ph.D.

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.

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

(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.