Machine Learning Research Engineer (1 Year Fixed Term)

Machine Learning Research Engineer (1 Year Fixed Term)

Join to apply for the Machine Learning Research Engineer (1 Year Fixed Term) role at Stanford University School of Medicine

Machine Learning Research Engineer (1 Year Fixed Term)

Join to apply for the Machine Learning Research Engineer (1 Year Fixed Term) role at Stanford University School of Medicine

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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 models on large-scale data of neuronal recordings - multimodal models, i.e., digital twins of a primate brain, that can relate unprecedented amounts of 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 and frontier 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

• Implement and optimize the latest machine learning algorithms / models to train multimodal foundation models on neural data
• Develop and maintain scalable, efficient, and reproducible machine-learning pipelines
• Conduct large-scale ML experiments, using the latest MLOps platforms
• Run large-scale distributed model training on high-performance computing clusters or cloud platforms
• Collaborate with machine learning researchers, data scientists, and systems engineers to ensure seamless integration of models and infrastructure
• Monitor and optimize model performance, resource utilization, and cost-effectiveness
• Stay up-to-date with the latest advancements in machine learning tools, frameworks, and methodologies
• Other duties may also be assigned

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

2+ years of practical experience in implementing and optimizing machine learning algorithms with distributed training using common libraries (e.g. Ray, DeepSpeed, HF Accelerate, FSDP)

Strong programming skills in Python, with expertise in machine learning frameworks like TensorFlow or PyTorch

Experience with orchestration platforms

Experience with cloud computing platforms (e.g., AWS, GCP, Azure) and their machine learning services

Familiarity with MLOps platforms (e.g. MLflow, Weights & Biases)

Strong understanding of software engineering best practices, including version control, testing, and documentation

Preferred Qualifications

Familiarity with training, fine tuning, and quantization of LLMs or multimodal models using common techniques and frameworks (LoRA, PEFT, AWQ, GPTQ, or similar)

Familiarity with modern big data tools and pipelines such as Apache Spark, Arrow, Airflow, Delta Lake, or similar

Experience with AutoML and Neural Architecture Search (NAS) techniques

Contributions to open-source machine learning projects or libraries

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

40 pounds.

Working Conditions

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.

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.

Seniority level

Seniority level

Entry level

Employment type

Employment type

Full-time

Job function

Job function

Engineering and Information Technology

Industries

Higher Education

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