Machine Learning Research Scientist (1 Year Fixed Term)

Machine Learning Research Scientist (1 Year Fixed Term) – Stanford/The Enigma ProjectLead 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 & ResponsibilitiesDesign and implement large-scale multimodal deep learning architectures that relate sensory inputs to neuronal correlates of perception, action, and cognitionDevelop novel computational approaches for training and optimizing frontier models on unprecedented amounts of neural dataProvide technical leadership in distributed training systems and model optimization techniquesGuide cross-functional teams in establishing technical frameworks and evaluation metrics for brain foundation modelsCommunicate research findings through publications, presentations, workshops and research blogsStay ahead of the latest developments in machine learning and neuroscience, and propose innovative solutions to advance the project's goalsOther duties may also be assignedQualificationsEducation & 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 experienceAt least 2+ years of practical experience in training, fine-tuning, and using multimodal deep learning modelsStrong publication record in top-tier machine learning conferences and journals, particularly in areas related to multimodal modelingStrong programming skills in Python and deep learning frameworksDemonstrated ability to lead research projects and mentor othersAbility to work effectively in a collaborative, multidisciplinary environmentPreferred QualificationsBackground in theoretical neuroscience or computational neuroscienceExperience processing and analyzing large-scale, high-dimensional data from different sourcesExperience with cloud computing platforms (e.g., AWS, GCP, Azure) and their ML servicesFamiliarity 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 similarExperience with large-scale distributed model training frameworks (e.g., Ray, DeepSpeed, HF Accelerate, FSDP)Education & Experience (required) continuedAs applicable to the role, see above for details on required education and experience.Knowledge, Skills and AbilitiesExpert knowledge of engineering principles and related natural sciencesDemonstrated project leadership experienceExperience leading and/or managing technical professionalsApplicationsTo 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 InformationLocation: Stanford UniversityWork Arrangement: On Site #J-18808-Ljbffr

Created: 2025-09-24