Inside Higher Ed
Machine Learning Research Scientist (1 Year Fixed Term)
Inside Higher Ed, Palo Alto, California, United States, 94306
Machine Learning Research Scientist (1 Year Fixed Term) – Stanford/The Enigma Project
Lead and contribute to the Enigma Project, a research initiative within the Department of Ophthalmology at Stanford University School of Medicine, focused on understanding the computational principles of natural intelligence using artificial intelligence. The project aims to create a foundation model of the brain, capturing relationships between perception, cognition, behavior, and neural activity, and to align AI 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 train frontier multimodal models on large-scale data of neuronal recordings that relate sensory input to neuronal correlates of perception, action, cognition, and intelligence. Candidates should have expertise in modern deep learning libraries (preferably PyTorch) and recent developments in multimodal foundation models. This position offers a vibrant academic environment at Stanford, with collaboration across computational neuroscience and deep learning disciplines. 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 Qualifications
Education & Experience (required):
Bachelor\'s degree and five years of relevant experience, or combination of education and relevant experience. 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 multimodal deep learning models Strong publication record in top-tier machine learning conferences and journals, particularly in areas related to multimodal 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 processing and analyzing large-scale, high-dimensional data from different sources Experience with cloud computing platforms (e.g., AWS, GCP, Azure) and their ML 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 LoRA, PEFT, AWQ, GPTQ, or similar Experience with large-scale distributed model training frameworks (e.g., Ray, DeepSpeed, HF Accelerate, FSDP) Education & Experience (required) continued
As applicable to the role, see above for details on required education and experience. Knowledge, Skills and Abilities
Expert knowledge of engineering principles and related natural sciences Demonstrated project leadership experience Experience leading and/or managing technical professionals Applications
To apply, please follow project guidelines and submit your CV and a one-page statement of interest to the designated contact. The job duties listed are typical examples and may vary by department or program needs. Additional Information
Location: Stanford University Work Arrangement: On Site
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Lead and contribute to the Enigma Project, a research initiative within the Department of Ophthalmology at Stanford University School of Medicine, focused on understanding the computational principles of natural intelligence using artificial intelligence. The project aims to create a foundation model of the brain, capturing relationships between perception, cognition, behavior, and neural activity, and to align AI 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 train frontier multimodal models on large-scale data of neuronal recordings that relate sensory input to neuronal correlates of perception, action, cognition, and intelligence. Candidates should have expertise in modern deep learning libraries (preferably PyTorch) and recent developments in multimodal foundation models. This position offers a vibrant academic environment at Stanford, with collaboration across computational neuroscience and deep learning disciplines. 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 Qualifications
Education & Experience (required):
Bachelor\'s degree and five years of relevant experience, or combination of education and relevant experience. 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 multimodal deep learning models Strong publication record in top-tier machine learning conferences and journals, particularly in areas related to multimodal 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 processing and analyzing large-scale, high-dimensional data from different sources Experience with cloud computing platforms (e.g., AWS, GCP, Azure) and their ML 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 LoRA, PEFT, AWQ, GPTQ, or similar Experience with large-scale distributed model training frameworks (e.g., Ray, DeepSpeed, HF Accelerate, FSDP) Education & Experience (required) continued
As applicable to the role, see above for details on required education and experience. Knowledge, Skills and Abilities
Expert knowledge of engineering principles and related natural sciences Demonstrated project leadership experience Experience leading and/or managing technical professionals Applications
To apply, please follow project guidelines and submit your CV and a one-page statement of interest to the designated contact. The job duties listed are typical examples and may vary by department or program needs. Additional Information
Location: Stanford University Work Arrangement: On Site
#J-18808-Ljbffr