Research Software Engineer; Data Science and AI Institute

The RSSN and DSAI are seeking a Research Software Engineer (RSE) who will devote 50% of their time as Technical Lead for the Research Software Support Network (RSSN) and 50% as an RSE for Data Science and AI Institute (DSAI). The RSE will report to the DSAI Director of Engineering while also having a dotted line reporting relationship to the RSSN Program Manager.

The RSSN role is currently funded for two years. At the end of that funding, this position will remain, but time allocations between the two roles may shift.

DSAI is a pan-institutional initiative at Johns Hopkins to advance artificial intelligence and its applications, in part through investments in the software engineering, data science, and machine learning space. DSAI is focused on revolutionizing discovery by advancing artificial intelligence that evolves collaboratively with human intelligence, combining the strengths of each for the betterment of society and the world in which we live.

DSAI will bring together the mathematical, computational, and ethical foundations of AI with the domains of Health & Medicine, Scientific Discovery, Engineered Systems, Security & Safety, and People, Policy & Governance.

The RSE will bring a strong academic background and relevant experience in industry focused on designing and building state-of-the-art AI models, data science techniques and applications across diverse domains. The successful candidates will work at the cutting edge of modern science in collaboration with DSAI-affiliated faculty at Johns Hopkins University (JHU) on projects ranging from consulting and short-term service engagements to large, multiyear AI and data science initiatives and applications.

DSAI will address the growing demand for high-quality professional software engineers within academia who can build dynamic, scalable, open software to facilitate accelerated scientific discovery across fields.

Governed by a community of campus partners, including DSAI, the RSSN is a new centralized resource hub connecting researchers who develop software with essential support services across JHU. The RSSN will provide pathways to expertise, tools, and best practices, enabling researchers and software engineers of all skill levels to create more robust and sustainable software for scientific research.

• Provides technical expertise for a new campus-wide initiative connecting researchers who develop software with support services across Johns Hopkins.
• Contributes to the development of a comprehensive research software assessment instrument.
• Conducts technical analyses during project assessments.
• Provides engineering perspectives to help researchers identify project goals and access appropriate support pathways.
• Must have a collaborative, empathetic approach, serving as a partner to researchers rather than an evaluator with an understanding that many people hesitate to ask for help due to concerns about how it may be perceived.
• Works with moderate independence under the supervision of the Program Manager, collaborating closely with the Project Administrator on day-to-day assessment operations.
• Develops and refines the Research Software Assessment Instrument and Assessment Workflow (evaluation criteria, technical analysis frameworks)
• Provides technical analysis of research software projects, covering code quality, architecture, infrastructure needs, sustainability considerations, and alignment with best practices
• Documents and creates training materials for the Assessment process.
• Evaluates the Assessment accuracy and appropriateness of recommended support pathways.
• Contributes to engineering perspectives in RSSN governance and planning discussions.
• Evaluates research software projects created by others and provides guidance on development practices, sustainability planning, and appropriate support pathways aimed at increasing the impact of such projects, though not directly maintaining or developing software systems.

• Participates in ground-breaking research projects that need advanced software solutions requiring expertise in software engineering not commonly found in scientific collaborations.
• The projects may require the creation of AI/ML solutions using the latest deep learning libraries trained on state-of-the-art hardware.
• Projects may also involve analysis of massive data sets either in the cloud or on premises.
• They may require creation of novel data science techniques, software pipelines for processing of real-time high-frequency data processing workflows and may need the design of complex database models for storing and disseminating scientific data sets.
• Some projects may require deep engagement, possibly leading to co-authorship on scientific publications, while others may involve a more casual consulting engagement.
• They may require software solutions developed from scratch or refactoring existing solutions to make them conform to industry standards (quality, efficiency, reusability, robustness, portability,…