Machine Learning Research Engineer (1 Year Fixed Term)

Stanford Blood Center; Stanford (CA); On-site; USD 126,000 - 152,000; Full time

Job summary

Join a pioneering research team at a prestigious institution dedicated to unraveling the mysteries of intelligence through cutting-edge AI and neuroscience. In this role, you will implement advanced machine learning algorithms to train multimodal models that simulate the brain's intricate workings. You will collaborate with a vibrant group of engineers and scientists, leveraging state-of-the-art computing infrastructure and unique datasets. This position offers a competitive salary and benefits, along with a collaborative environment that fosters innovation and professional growth. If you are passionate about pushing the boundaries of technology and science, this opportunity is for you.

Benefits

Access to unique datasets

State-of-the-art computing infrastructure

Strong mentoring in career development

Collaborative environment

Competitive salary and benefits

Qualifications

• Master's or Bachelor's degree with relevant industry experience.
• Experience in machine learning algorithms and distributed training.

Responsibilities

• Implement and optimize machine learning models on neural data.
• Collaborate with researchers and engineers for model integration.

Skills

Machine Learning Algorithms

Python Programming

TensorFlow

PyTorch

Distributed Training

Cloud Computing (AWS, GCP, Azure)

MLOps Platforms

Software Engineering Best Practices

Education

Master's Degree in Computer Science

Bachelor's Degree in Computer Science

Tools

Ray

DeepSpeed

MLflow

Weights & Biases

Job description

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.

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.

Role & Responsibilities:

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

What we offer:

• An environment in which to pursue fundamental research questions in AI and neuroscience.
• A vibrant team of engineers and scientists in a project dedicated to one mission.
• 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: [e-mail]

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):
• Thorough knowledge of the principles of engineering and related natural sciences.
• Demonstrated project management experience.

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 $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.