Machine Learning Research Scientist (1 Year Fixed Term)

About the Role

Key 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

Requirements

• 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 multi-modal deep learning models
• Strong publication record in top-tier machine learning conferences and journals, particularly in multi-modal 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

Nice to Have

• Background in theoretical neuroscience or computational neuroscience
• Experience in processing and analyzing large-scale, high-dimensional data from different sources
• Experience with cloud computing platforms (e.g., AWS, GCP, Azure) and their machine learning 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 techniques like LoRA, PEFT, AWQ, GPTQ
• Experience with large-scale distributed model training frameworks (e.g., Ray, DeepSpeed, HF Accelerate, FSDP)

Qualifications

• Bachelor's degree and five years of relevant experience, or a combination of education and relevant experience
• Expert knowledge of engineering principles and related natural sciences
• Demonstrated project leadership experience
• Experience leading and managing technical professionals

Benefits & Perks

• A rich environment for fundamental research in AI and neuroscience
• 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
• Strong mentoring in career development
• Location at Stanford University with access to its research community