Job Description
The role involves ensuring the accuracy and quality of AI systems applied to battery materials research, including data validation, model tuning, and scientific reasoning, to advance next-generation battery technologies using AI and material science expertise.
Key Responsibilities
- Translate battery materials knowledge into structured, AI-trainable data.
- Lead data validation, cleaning, and annotation processes to ensure dataset quality.
- Oversee creation of benchmark datasets and design 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.
- Knowledge of 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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