Machine Learning Research Engineer

Machine Learning Research Engineer (1 Year Fixed Term) at Stanford University summary:

This role involves developing and optimizing large-scale multimodal machine learning foundation models using neural data to better understand brain computation. The Machine Learning Research Engineer will leverage deep learning frameworks, MLOps platforms, and distributed training on HPC or cloud systems to advance research in AI and computational neuroscience. The position is based at Stanford University and offers a collaborative environment with cutting-edge infrastructure and interdisciplinary expertise.

The Enigma Project (enigma project.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:

• 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, 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:

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, Deep Speed, HF Accelerate, FSDP)
Strong programming skills in Python, with expertise in machine learning frameworks like Tensor Flow or Py Torch

Experience with orchestration platforms

Experience with cloud…