[Hiring] Full Stack Materials Database Programmer for ML/AI Integration @Argonne National Laboratory

Role Description

Q-NEXT and the Argonne Quantum Foundry invites applications for a Full-Stack Database Programmer / Technical Developer to support the development, implementation, and maintenance of data infrastructure used in materials synthesis, model and experiment interoperability, autonomous discovery and closed-loop AI/ML frameworks. The successful candidate will contribute to improving the laboratory’s quantum materials data pipeline by helping consolidate legacy, fragmented systems into a unified, high-performance, and scalable database architecture based on the LiST codebase model.

Joining a dynamic, collaborative team working across Q-NEXT, Penn. State, the Argonne community, and partners at the University of Chicago, the candidate will support the acceleration of Q-NEXT’s materials development thrusts and future AI/ML/modeling-forward efforts.

The primary focus of this position is to support the design and implementation of robust relational and non-relational database schemas capable of handling heterogeneous datasets, i.e. a backend robust sample lifetime tracking database. The database infrastructure will interface directly with synthesis and characterization tools, automated cluster tool platforms, and automated synthesis hardware, enabling real-time data ingestion, structural logging, and API-driven access for physics-bound AI models and broader efforts focusing on leveraging both predictive, experimental, and simulated datasets.

The programmer will collaborate with post-doctoral researchers, staff scientists, and external partners such as LiST within Q-NEXT to:

• Gather data and workflow requirements
• Develop and test code
• Document database structures and software components
• Assist in improving data access and query performance for project needs

Responsibilities may also include supporting application components, APIs, dashboards, and data processing workflows using established software development practices.

Qualifications

• Experience working on scientifically focused materials databases (e.g. LiST) strongly valued.
• Experience in developing, normalizing, and maintaining production-grade relational (e.g., PostgreSQL, MySQL) and non-relational (e.g., MongoDB, NoSQL structures) databases.
• Experience with backend development in C#, .NET, or Python (FastAPI, Flask, or Django).
• Familiarity with frontend technologies such as React or Next.js to support internal data dashboards and visualization tools.
• Experience supporting automated data pipelines, streaming data ingestion, and interfacing code with experimental hardware or instrumentation control systems.
• Excellent written and oral communication skills; ability to work effectively within an interdisciplinary team of physicists, materials scientists, and automation engineers.
• Experience with data curation and version control via Git.
• Experience with containerization (Docker, Kubernetes) and deploying applications within enterprise or cloud-based scientific computing environments.
• Experience working with heterogeneous scientific data and metadata formats (e.g., HDF5, JSON, raw instrument logs) or working within a research/laboratory environment.
• Ability to work independently on assigned tasks under general supervision.
• High adherence to evidence-based software architecture and clean code practices.
• Ability to model Argonne's core values of impact, safety, respect, integrity, and teamwork.
• Bachelor's degree with 2+ years of experience, or equivalent.

Requirements

• Familiarity with machine learning loops or active learning frameworks.
• Familiarity with orchestration tools (e.g., Airflow, Prefect) for managing scientific data workflows.
• Knowledge of web security protocols, user authentication (OAuth2, JWT), and role-based access control.

Benefits

• The expected hiring range for this position is $69,750.00 - $108,810.00.
• Comprehensive benefits are part of the total rewards package.