Job Description
The role involves ensuring the quality and accuracy of AI systems related to battery materials and chemistry by translating scientific knowledge into structured data, validating datasets, and collaborating on AI model development to advance next-generation battery technologies.
Key Responsibilities
- Translate battery materials knowledge into structured, AI-trainable data
- Lead data validation, cleaning, and annotation processes to ensure dataset quality
- Oversee creation and management of benchmark datasets and domain-specific evaluations
- Collaborate with AI teams to ensure model correctness, reliability, and scientific reasoning
- Implement techniques to tune and validate AI models against scientific principles
- Apply Battery Informatics principles across data pipelines and modeling efforts
Requirements
- Ph.D. in Chemical Engineering with a focus on Lithium battery systems, Materials Science, or a closely related computational domain field.
- Deep expertise in battery materials, particularly knowledge required to convert complex, real-world data into AI-trainable formats.
- Proven experience in data validation, annotation, and benchmark creation for complex scientific or engineering datasets.
- Experience working with advanced AI systems, including familiarity with LLM pipelines and the principles of multi-agent orchestration.
- Experience in roles such as Applied Scientist in Molecular Materials AI or similar specialist roles focused on AI system quality in a scientific domain.
- Experience as a Computational battery AI specialist.
- Ability to translate deep Battery Materials Knowledge and next-generation battery concepts into correctly structured, high-quality, AI-trainable data.
- Ability to lead processes for rigorous data validation, cleaning, and annotation to ensure consistency and correctness across all datasets.
- Experience overseeing the creation and management of benchmark datasets and designing domain-specific multimodal evaluations to test model accuracy.
- Ability to partner closely with AI architecture and engineering teams to ensure the correctness, reliability, and scientific reasoning quality of models, including LLM creation and multi-agent orchestration.
- Experience implementing techniques, including those inspired by reinforcement learning RLHF, to tune and validate model behavior against established scientific principles.
- Ability to ensure that resulting models accurately understand molecular chemistry, materials data, and complex scientific reasoning in the battery domain.
- Experience applying Battery Informatics principles across data pipelines and modeling efforts.
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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