Job Description
The role involves designing and implementing advanced AI architectures for materials discovery and battery technology, focusing on multimodal models, high-performance computing, and explainability within a scientific research environment.
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 environments, focusing on GPU programming and scaling neural network training and inference
- Optimize machine learning frameworks like JAX within HPC systems and cluster computing environments
- Create automated data-labeling and behavioral encoding models to enhance Molecular AI training and data efficiency
- Apply scientific ML principles to biological and materials datasets to translate biological computation concepts into AI solutions 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 applying Scientific Machine Learning (ML) principles to complex molecular and battery datasets, including developing automated data-labeling and behavioral encoding models to enhance training and data efficiency.
- Ability to architect novel AI systems and deep neural architectures such as Transformers and CNNs inspired by principles of systems neuroscience and neural coding principles.
- Experience designing and implementing large-scale multimodal foundation models and agentic AI systems capable of complex reasoning over molecular and battery datasets.
- Ability to develop methods for model interpretability, representation engineering, and causal reasoning to ensure AI results are explainable and trustworthy for materials science.
- Experience optimizing complex ML frameworks like JAX within systems and cluster computing environments such as Singularity.
- Knowledge of high-performance computing (HPC) software development, including scaling training and inference efficiency of large neural networks.
- Experience with advanced mathematical and machine learning frameworks like JAX and Julia is preferred.
- Knowledge of Bayesian inference and working within specialized container computing environments like Singularity is preferred.
- Experience with the design and application of agentic AI systems and multimodal reasoning architectures is preferred.
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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