Research Software Engineer / Technical Lead, Research Software Support Network

Code4lib; Baltimore (MD); On-site; USD 80,000 - 120,000; Full time

Benefits offered by this job

Access to cutting-edge technology

Collaboration with leading researchers

Opportunities for professional development

Job summary

A leading research university in Maryland is seeking a Research Software Engineer to serve as a Technical Lead for the Research Software Support Network while also engaging in consultation for the Data Science and AI Institute. Key responsibilities include providing technical expertise for research software initiatives, evaluating software quality, and developing training materials. The ideal candidate will have strong software engineering experience and a Master's degree in a relevant field. The position offers a unique opportunity to drive innovation in AI and data science.

Qualifications

• 3+ years of software development experience in large or complex projects.
• Master's in Computer Science or related field.
• Ability to communicate technical concepts to non-technical stakeholders.

Responsibilities

• Provide technical expertise for a campus-wide initiative.
• Conduct technical analyses during project assessments.
• Develop training materials for the assessment process.
• Contribute to advanced software solutions for research projects.

Skills

Code quality evaluation

Expert-level Python

R, C/C++, JavaScript/TypeScript, Julia

Data science techniques

Familiarity with Docker and Singularity

Fluency in Linux

Excellent verbal and written communication

Analytical skills

Leadership and self-direction

Education

Master's in Computer Science

PhD in relevant field

Tools

Git

Statistical modeling tools

Development tools and libraries

Job description

Research Software Engineer / Technical Lead, Research Software Support Network

Johns Hopkins University seeks 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 the Data Science and AI Institute (DSAI). The RSE will report to the DSAI Director of Engineering and maintain a dotted‑line relationship with the RSSN Program Manager in the Open Source Programs Office (OSPO) of JHU Sheridan Libraries. The RSSN role is funded for two years, after which time the 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 through investments in software engineering, data science, and machine learning. It brings together the mathematical, computational, and ethical foundations of AI with domains of Health & Medicine, Scientific Discovery, Engineered Systems, Security & Safety, and People, Policy & Governance. The RSE will work at the cutting edge of modern science in collaboration with DSAI‑affiliated faculty on projects ranging from short‑term consultations to large, multiyear AI and data science initiatives.

Governed by a community of campus partners, the RSSN is a centralized hub connecting researchers who develop software with essential support services across JHU. It provides 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.

Specific Duties and Responsibilities

RSSN

• Provides technical expertise for a new campus‑wide initiative connecting researchers with support services.
• 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.
• Maintains a collaborative, empathetic approach, serving as a partner rather than an evaluator, understanding that many people hesitate to ask for help.
• Works independently 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 it does not maintain or develop software systems directly.

DSAI

• Participates in groundbreaking research projects requiring advanced software solutions and expertise in software engineering not commonly found in scientific collaborations.
• Projects may require creation of AI/ML solutions using the latest deep learning libraries trained on state‑of‑the‑art hardware.
• Projects may involve analysis of massive datasets either in the cloud or on premises.
• Projects may require novel data science techniques, software pipelines for real‑time high‑frequency data processing, and design of complex database models for storing and disseminating scientific datasets.
• Some projects may require deep engagement, potentially leading to co‑authorship on scientific publications; others may involve casual consulting engagements.
• Projects may require software solutions developed from scratch or refactored to meet industry standards (quality, efficiency, reusability, robustness, portability, documentation, etc.).
• A high‑level goal of DSAI is to translate individual project efforts into frameworks and template patterns for sustainable scientific infrastructure benefiting future projects.

Special Knowledge, Skills, and Abilities Required

• Software engineering experience with demonstrated ability to evaluate code quality, architecture, and technical debt.
• Experience developing software using programming languages common in academic research, such as expert‑level Python, R, C/C++, JavaScript/TypeScript, Julia; willingness to learn other languages as needed.
• Experience with academic/scientific computing environments.
• Familiarity with modern software engineering best practices, such as Git source control, peer code review, test‑driven development, build automation, and continuous integration/continuous delivery.
• Experience designing, developing, and applying state‑of‑the‑art data science techniques to the analysis of large datasets (statistical and mathematical modeling, data transformation, ETL, information extraction, data modeling, large‑scale computing architectures, distributed pipelines, real‑time streaming).
• Familiarity with software containerization technologies such as Docker and Singularity.
• Fluency in Linux and related tools.
• Excellent verbal and written communication, including the ability to communicate technical concepts accessibly to non‑technical stakeholders.
• Strong analytical skills for assessing project needs and recommending appropriate support pathways.
• Demonstrated leadership and self‑direction.
• Willingness to teach and mentor others informally and in short‑course format.
• Willingness to continually learn new tools and techniques as needed.

Minimum Qualifications

• Master's in Computer Science, Engineering, a Quantitative Discipline, or a Domain Science with strong computational components.
• Three or more years of software development experience in large or complex projects.
• Additional education may substitute for required experience and additional related experience may substitute for required education beyond a high school diploma or graduation equivalent, to the extent permitted by the JHU equivalency formula.

Preferred Qualifications

• PhD in computer science, engineering, a quantitative discipline, or a domain science with strong computational components.
• Experience with AI/ML, vision, NLP, bioinformatics, and/or mathematical or computational libraries.
• Experience developing, training, fine‑tuning, and applying LLMs and/or foundational models.
• Experience deploying AI models onto clinical platforms.
• Experience with large‑scale scientific simulations or simulations of air/terrestrial/sea vehicles.
• Familiarity with data formats common in scientific domains such as medical imaging, genomic sequences, proteins, chemical structures, geospatial, oceanographic, or health record data.
• Experience in CUDA GPU programming.
• Experience authoring open‑source Python packages in PyPI.
• Familiarity with RESTful web service principles and development.
• Familiarity with SQL and relational database principles and development.
• Familiarity with cloud development and deployment.
• Experience working in an academic research environment or with academic researchers.
• Understanding of research software sustainability considerations (governance, community engagement, long‑term maintenance planning).
• Familiarity with FAIR principles for research software (findable, accessible, interoperable, reusable).
• Experience working with open‑source software communities, including governance and community adoption initiatives.
• Experience mentoring or advising others on software development practices.