Scientist, Molecular AI Architecture

SES

Woburn, Massachusetts

Full Time

Posted March 17, 2026

Energy Storage

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Job Description

A research scientist role focused on developing advanced AI architectures and models for materials discovery and battery technology, integrating machine learning, neuroscience principles, and high-performance computing to accelerate innovation 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 AI results are explainable and trustworthy.
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 complex molecular and battery datasets, translating 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 principles to complex molecular and battery datasets, translating biological computation concepts into practical AI solutions for materials discovery.