Full Stack Materials Database Programmer for ML/AI Integration

Argonne National Laboratory•Lemont, IL

1d•$69,750 - $108,810•Onsite

About The Position

Q-NEXT and the Argonne Quantum Foundry are seeking a Full-Stack Database Programmer / Technical Developer to support the development, implementation, and maintenance of data infrastructure for materials synthesis, model and experiment interoperability, autonomous discovery, and closed-loop AI/ML frameworks. The role involves consolidating legacy systems into a unified, high-performance, and scalable database architecture based on the LiST codebase model. The candidate will join a dynamic, collaborative team and support the acceleration of Q-NEXT’s materials development thrusts and future AI/ML/modeling-forward efforts. The primary focus is on designing and implementing robust relational and non-relational database schemas for heterogeneous datasets, specifically a backend sample lifetime tracking database. This infrastructure will interface 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 AI models and broader data leveraging efforts. The programmer will collaborate with researchers and external partners to gather requirements, develop and test code, document database structures and software components, and improve data access and query performance. Responsibilities may also include supporting application components, APIs, dashboards, and data processing workflows.

Requirements

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.
Experience supporting automated data pipelines and streaming data ingestion.
Experience 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).
Experience 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.

Nice To Haves

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.

Responsibilities

Design and implement robust relational and non-relational database schemas capable of handling heterogeneous datasets.
Develop a backend robust sample lifetime tracking database.
Interface database infrastructure directly with synthesis and characterization tools, automated cluster tool platforms, and automated synthesis hardware.
Enable real-time data ingestion and structural logging.
Provide API-driven access for physics-bound AI models and broader efforts focusing on leveraging predictive, experimental, and simulated datasets.
Collaborate with post-doctoral researchers, staff scientists, and external partners 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.
Support application components, APIs, dashboards, and data processing workflows using established software development practices.