Software Internship - AI & Battery Informatics

Are you a code-savvy engineer who thrives on high-performance data and agentic systems? We are looking for an AI-forward, battery science-centric software intern to bridge the gap between electrochemical R&D and production-grade data science. You will complement our Battery Engineering & R&D engineering teams by focusing on the "heavy lifting" of data systems, computer vision, and AI-forward applications. You are someone who views data as a high-performance asset and wants to build the autonomous workflows that will determine how we discover the next generation of energy storage.

Project examples could include designing an AI Agent to browse experimental databases, analyze ECT data, and summarize performance trends. Developing a production CV pipeline that automates fiber/particle quantification in seconds, replacing manual measurement. Or creating the "plumbing" for physics-based models to ingest millions of rows of cycling data via high-performance manipulation.

Responsibilities and Duties

• Build and deploy autonomous AI agents to automate experimental management, enabling researchers to query, summarize, and reason over complex datasets.
• Develop automated pipelines for eg. SEM (Scanning Electron Microscopy) and other characterization techniques to automate feature extraction (e.g., particle morphology, particle sizing, feature extraction, and defect detection).
• Data orchestration and optimization support for modeling teams with build and validate physics-based and ML- models (Transformers, GNNs) to predict battery performance.
• Design and optimize data pipelines capable of handling massive, high-frequency battery datasets (cycling, modeling) using advanced frameworks for out-of-memory processing.
• Support the development of backend pipelines, FastAPI tools, and internal dashboards (Hex, React,js) to bring AI-driven insights to a wider audience.

Knowledge and Skill Requirements

• BSc (or preferably MSc/currently enrolled in a Ph.D. program) in Computer Science, Materials Science, or a related field.
• Mastery of Python scientific libraries (NumPy, SciPy, Pandas). Expert-level experience with high-performance frameworks like Polars, Vaex, or Dask for massive datasets.
• Experience building agentic workflows (e.g., LangGraph, CrewAI) and utilizing cloud-native AI functions such as Snowflake Cortex.
• Proficiency in OpenCV or deep learning-based image segmentation (e.g., SAM) for material characterization.
• Strong proficiency in building production-ready code, including K8s, Docker, and CI/CD pipelines.
• Basic understanding of Li-ion battery chemistry, materials processing, and electrochemical testing.
• Proficiency in using AI coding tools (e.g., Claude Code, Copilot) and Git-based collaboration.