Research Software Engineer (Data Science and AI Institute)

Johns Hopkins, founded in 1876, is America's first research

university and home to nine world-class academic divisions working

together as one university.

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.

Specific Duties and Responsibilities

RSSN

• 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.

DSAI

• 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, documentation, etc.).

• It is a high-level goal of DSAI to translate the efforts for

individual projects 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 Python (expert-level), R,

C/C++, JavaScript/TypeScript, Julia; willingness to learn other

languages as may be 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, building automation and continuous integration /

continuous delivery.

• Experience designing, developing and applying state-of-the-art

data science techniques to the analysis of large data sets. Areas

of relevant expertise include design and development of statistical

and mathematical models of data, data transformation, ETL and

information extraction, data modeling of complex scientific

datasets, architectures for computing with large datasets,

distributed computational pipelines and real-time data streaming

architectures.

• Familiarity with software containerization technologies such as

Docker and Singularity.

• Fluency in the Linux operating system 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 both 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/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 heath 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.

Classified Title: Scientific Software Engineer

Job Posting Title (Working Title): Research Software Engineer (Data

Science and AI Institute)

Role/Level/Range: APPTSTAF/01/ST

Starting Salary Range: Commensurate w/exp.

Employee group: Full Time

Schedule: M-F, 37.5 hrs wkly

FLSA Status: Exempt

Location: Hybrid/Mount Washington Campus

Department name: DSAI Institute

Personnel area: Whiting School of Engineering

The successful candidate(s) for this position will be subject to a

pre-employment background check.

If you are interested in applying for employment with The Johns

Hopkins University and require special assistance or accommodation

during any part of the pre-employment process, please contact the

HR Business Services Office at jhurecruitment@jhu.edu. For TTY

users, call via Maryland Relay or dial 711