Job Description
The role involves designing and implementing advanced AI architectures for materials discovery and battery technology, focusing on large-scale multimodal models, high-performance computing, and explainability to accelerate innovations in energy storage solutions.
Key Responsibilities
- Design and implement novel AI systems and deep neural architectures inspired by neuroscience principles.
- Develop large-scale multimodal foundation models and agentic AI systems for complex reasoning over molecular and battery datasets.
- Create methods for model interpretability, representation engineering, and causal reasoning to ensure explainability and trustworthiness of AI results.
- Lead software development for high-performance computing, focusing on GPU programming and scaling neural network training and inference.
- Optimize machine learning frameworks like JAX within HPC environments to improve efficiency.
- Create automated data-labeling and behavioral encoding models to enhance molecular AI training and data efficiency.
- Apply scientific machine learning principles to complex molecular and battery datasets for materials discovery.
Requirements
- Ph.D. in Computational and Systems Biology, Computational Neuroscience, or a closely related quantitative field.
- Deep, demonstrated expertise in systems neuroscience, machine learning, and the design and implementation of deep neural architecture.
- Proven experience with software development for High-Performance Computing (HPC) environments, including expert-level GPU programming.
- Practical experience in designing and training foundation models and working with concepts like multi-agent reasoning models.
- Demonstrated work in model interpretability and representation engineering applied to complex scientific data.
- Experience creating automated data-labeling and behavioral encoding models specifically designed to enhance Molecular AI training and data efficiency.
- Ability to develop and implement large-scale multimodal foundation models and agentic AI systems capable of complex reasoning over molecular and battery datasets.
- Experience in designing AI architectures such as Transformers and CNNs inspired by principles of systems neuroscience and neural coding principles.
- Experience in optimizing complex ML frameworks like JAX within systems and cluster computing environments such as Singularity.
- Knowledge of methods for model interpretability, representation engineering, and causal reasoning to ensure AI results are explainable and trustworthy for materials science.
Benefits & Perks
highly competitive salary
robust benefits package including comprehensive health coverage
attractive equity stock options program
opportunity to contribute to meaningful scientific projects with broad public impact
work in a dynamic, collaborative, and innovative environment
significant opportunities for professional growth and career development
access to state-of-the-art facilities and proprietary technologies
Ready to Apply?
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