Machine Learning and Neuro-Artificial Intelligence Specialist

Yale University is hiring a Machine Learning and Neuro-Artificial Intelligence Specialist for its Department of Neurosurgery and ARIA program. The role develops and evaluates machine-learning methods for large-scale multimodal human brain data, supports research projects, and collaborates with clinical and scientific teams.

Responsibilities

• Develop, train, and evaluate machine-learning models for multimodal human brain data
• Build models for neural representation learning, forecasting, decoding, and cross-modal analysis
• Contribute to the development of scalable brain foundation models across subjects, recording modalities, and tasks
• Work with large datasets and optimize training pipelines for GPU and HPC environments
• Collaborate with clinicians, neuroscientists, engineers, and other researchers on study design and analysis
• Maintain clear, reproducible code and documentation for model development and evaluation
• Contribute to manuscripts, conference presentations, and grant-related research activities
• Provide technical guidance to students and research staff when appropriate
• User Training: Provide advanced training and guidance to users of the research core
• Experiment Support: Collaborate with researchers on experimental design, execution, data analysis, and troubleshooting within the research core
• Facility/Instrument Support: Manage and oversee research projects within the research core
• Technique Development: Lead the implementation and optimization of advanced techniques and methodologies and develop standard operating procedures for research core services
• Data Analysis/Interpretation: Analyze and interpret complex scientific data, generating meaningful insights and recommendations
• Equipment Procurement: Assist in the selection, acquisition, and maintenance of research core equipment
• Professional Development: Stay updated with emerging research trends and technologies. Participate in professional development activities and scientific conferences
• Scientific Participation: Contribute scientific data for manuscript preparation and grant proposals
• Staff Training and Mentoring: Contribute to the training and mentoring of junior staff members
• Vendor Relations: Manage vendor relationships, negotiates contracts, and evaluates new technologies for optimal procurement processes
• Education and Outreach: Contribute to education and outreach activities within the core, including tours, events, etc

Preferred

• Proven experience with neural or physiological data, including iEEG/ECoG, EEG, fNIRS, fMRI, MEG, or related biosignals
• Proven experience with time-series modeling, neural forecasting, multimodal learning, or brain-computer interface research
• Proven experience with efficient attention or large-model training methods, including scaled-dot-product attention, FlashAttention, mixed precision, or activation checkpointing
• A research record in machine learning, computer vision, natural language processing, computational neuroscience, biomedical AI, or a related area
• Proven experience developing machine-learning or deep-learning models for research applications
• Neural data
• Physiological data
• iEEG/ECoG
• EEG
• fNIRS
• fMRI
• MEG
• Biosignal analysis
• Time-series modeling
• Neural forecasting
• Multimodal learning
• Brain-computer interfaces
• Efficient attention
• Large-model training
• Scaled-dot-product attention
• FlashAttention
• Mixed-precision computing
• Activation checkpointing
• Machine learning research
• Computer vision research
• Natural language processing
• Computational neuroscience
• Biomedical AI
• Machine-learning model development
• Deep-learning model development
• Research applications

Benefits

• Hybrid work arrangement

Company

• Tech career marketplace connecting professionals with recruiters
• Centennial, Colorado, USA
• https://www.or-quest.nl/
• Founded in 1990
• 201-500 employees

Qualifications

• Proven experience developing machine-learning or deep-learning models
• Proven experience with distributed or multi-GPU training and memory-efficient model training
• Familiarity with tools such as Slurm, CUDA, Docker or Apptainer, Conda, and scientific computing clusters
• Strong Python programming skills and practical experience with PyTorch or a comparable deep-learning framework
• Proven experience with modern neural-network architectures such as Transformers, self-supervised learning, representation learning, generative models, or foundation models
• Proven experience training models on GPUs and working in Linux and high-performance computing environments
• Bachelor's degree in a related discipline and four years of related experience or an equivalent combination of skills, education, and experience
• Proven experience with neural or physiological data, including iEEG/ECoG, EEG, fNIRS, fMRI, MEG, or related biosignals
• Proven experience with time-series modeling, neural forecasting, multimodal learning, or brain-computer interface research
• Proven experience with efficient attention or large-model training methods, including scaled-dot-product attention, FlashAttention, mixed precision, or activation checkpointing
• A research record in machine learning, computer vision, natural language processing, computational neuroscience, biomedical AI, or a related area
• Proven experience developing machine-learning or deep-learning models for research applications
• Bachelor's degree in a related discipline and four years of related experience or an equivalent combination of skills, education, and experience.

Required skills

• Activation Checkpointing, Apptainer, Brain-Computer Interface Research, CUDA, Conda, Deep Learning, Distributed and Multi-GPU Training, Docker, EEG, FlashAttention, Foundation Models, GPU Computing, Generative Models, High-Performance Computing, Linux, MEG, Machine Learning, Memory-Efficient Model Training, Mixed-Precision Training, Multimodal Learning, Neural Network Architectures, Neural and Physiological Data, PyTorch, Python, Representation Learning, Scaled Dot-Product Attention, Self-Supervised Learning, Slurm, Time-Series Modeling, Transformers, fMRI, fNIRS

Experience

• 4+ years