Applied AI Research Scientist

3 days ago

Boston, United States DeepRec.ai Full time

Data and Evaluation Applied AI Scientist (Battery Informatics and Scientific AI Systems) Compensation: $180k - $310k Join an exciting and rapidly expanding energy technology company as we grow our AI research division. We are seeking a talented Data and Evaluation Applied AI Scientist to help us create innovative AI systems that drive discovery in energy storage and next-generation battery materials. This pivotal role will focus on ensuring data correctness, enhancing scientific reasoning quality, and maintaining the integrity of battery domain knowledge essential for training advanced AI models. Our company merges material science with applied AI to pioneer high-energy lithium metal and lithium-ion battery technologies. We utilize an AI-enhanced discovery platform to design groundbreaking electrolytes, model electrochemical behaviors, and develop advanced materials with real-world applications spanning transportation, robotics, aerospace, and long-duration energy storage. What You Will Do Transform complex knowledge of battery materials into well-structured, high-quality datasets suitable for AI training. Lead thorough data validation, cleaning, and annotation processes to guarantee scientific accuracy. Develop benchmark datasets and create multi-modal evaluation suites for model testing. Collaborate with AI architecture teams to evaluate and refine model reasoning concerning chemistry, materials, and electrochemical concepts. Utilize techniques that enhance correctness and model alignment, including those inspired by human feedback systems. Ensure that AI models accurately grasp molecular behavior, materials interactions, and battery domain logic. Promote the use of battery informatics principles throughout our data pipelines and model development processes. What You Bring A PhD in Chemical Engineering with a concentration on lithium battery systems, Materials Science, or a closely related computational field. In-depth domain expertise in battery materials and electrochemical systems. Proven experience in converting scientific data into AI-adaptable structures. Hands-on experience with data validation, annotation, and benchmark dataset creation. Familiarity with LLM pipelines, AI evaluation, or multi-agent scientific workflows. A background in applied science or AI roles related to materials. Preferred Experience Practical experience with RLHF or other model tuning and alignment methodologies. Previous work in battery laboratories, materials informatics teams, or AI for Science groups. Experience in designing tailored evaluation frameworks for complex AI systems. Specialized knowledge as a computational battery AI expert.

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