Full Stack Materials Database Programmer for ML/AI Integration

• Develop, implement, and maintain data infrastructure for materials synthesis, model and experiment interoperability, and closed-loop AI/ML frameworks.
• Consolidate legacy systems into a unified, high-performance, scalable database architecture based on the LiST codebase model.
• Design robust relational and non-relational database schemas for heterogeneous datasets, enabling real-time data ingestion and API-driven access for AI models.
• Collaborate with researchers and partners to gather requirements, develop code, document structures, and improve data access and query performance.

This role accelerates quantum materials discovery by building robust data infrastructure that enables AI/ML-driven autonomous experimentation, directly advancing Q-NEXT's mission to develop transformative quantum technologies.

• Experience with production-grade relational (PostgreSQL, MySQL) and non-relational (MongoDB) databases, preferably in scientific materials databases (e.g., LiST).
• Full-stack development skills: backend in C#, .NET, or Python (FastAPI, Flask, Django); frontend in React or Next.js.
• Experience with automated data pipelines, streaming data ingestion, and interfacing with experimental hardware.
• Bachelor's degree with 2+ years of experience or equivalent; familiarity with Git, Docker, Kubernetes, and scientific data formats (HDF5, JSON).

Salary range: $69,750 - $108,810 per year. Comprehensive benefits package including healthcare insurance. Argonne is an equal opportunity employer.

Argonne National Laboratory is a multidisciplinary science and engineering research center dedicated to solving grand challenges in energy, environment, and national security. Q-NEXT is a Department of Energy National Quantum Information Science Research Center focused on quantum materials and technologies.