Software Research Engineer

Overview

The Data Science and Artificial Intelligence group (DSAI) in the NIST Material Measurement Laboratory is

seeking a Research Software Engineer (RSE) to help build sustainable software infrastructure for AI-
enabled materials and chemical science. The successful candidate will provide both leadership and hands-
on engineering support for the development, maintenance, deployment, and long-term sustainability of

critical research software, databases, workflows, and public scientific infrastructure.
In this role you will work as an embedded technical team member across multiple DSAI projects,
participating in project planning, code review, architecture discussions, reproducibility efforts, and
deployment decisions. You will have the opportunity to shape software practices in a growing AI for
science group, contribute to public scientific datasets and benchmarks, and help translate research
prototypes into reliable software tools, simulation and benchmarking workflows, and measurement
capabilities used by NIST researchers and the broader community to accelerate development and
deployment of new materials technologies. The successful candidate will help prioritize engineering work
across projects in consultation with DSAI scientists, balancing near-term research needs with long-term
software sustainability.

Qualifications

• A Master’s degree in a STEM field, computer science, software engineering, or equivalent professional experience.
• Software Engineering Practice: Library and API design and release management, documentation for scientific users and developers, automated testing and continuous integration, code review and maintainability practices.
• Collaborative Leadership: The ability to work alongside researchers in adopting modern, collaborative software development workflows (e.g., git-based collaboration, issue tracking, lightweight project management).
• Data Engineering: Experience building data pipelines and reproducible workflows, for example designing SQL-backed data storage and provenance systems to support computational research.
• Scientific Computing: Demonstrated software engineering experience handling numerical and scientific data, with deep familiarity with the Python numerical computing ecosystem
• Documentation: Experience creating tutorials, examples, API documentation, or other user- facing materials for scientific software.
• Preferred Qualifications (Strong Plus): o Machine Learning Operations: Experience evaluating and deploying ML systems in reproducible research or production environments, with specific proficiency in PyTorch. o Open-source software: the ideal candidate will have a track record of contributions to open-source projects and community engagement. o Computational materials science: Familiarity with databases and workflows. o Performance Engineering: A track record of optimizing code for speed and efficiency, with experience using profiling and debugging tools. o GPU Computing: Familiarity with GPU-based packages, particularly those relating to machine learning interatomic potentials. o Web Development: Experience building and deploying robust web applications using frameworks such as FastAPI, Django, or similar.

Research Proposal

Key responsibilities will include but are not limited to:

• Design and deploy scientific databases supporting active research and public datasets.
• Build and maintain infrastructure for self-driving laboratory projects, including ongoing work on an autonomous metal-organic framework synthesis platform and high-speed synchrotron characterization project.
• Refactor, profile, test, and package prototype research code for long-term maintainability and performance.
• Support scientific machine learning workflows, benchmark suites, and reproducible ML evaluation pipelines.
• Develop a user-friendly, intuitive websites for unique scientific datasets, for example DSAI’s newly curated quantum transport database of semiconductor interfaces.