Research Software Engineer (Data Science and AI Institute)

Johns Hopkins University•Baltimore, MD

2d•Hybrid

About The Position

The Johns Hopkins Data Science and AI Institute (DSAI) is seeking multiple Research Software Engineers with strong academic and industry backgrounds. The role involves designing and building software for state-of-the-art AI and data science applications across diverse scientific domains. Successful candidates will work collaboratively with DSAI affiliated faculty on projects ranging from consulting to large, multiyear AI and data science initiatives. DSAI aims to address the demand for professional software engineers in academia who can build dynamic, scalable, open software to accelerate scientific discovery. DSAI engineers will be at the forefront of modern data-intensive science, where professionally developed software is crucial for success, and will contribute to building a substantive and professional-scale software engineering capability.

Requirements

Expert-level knowledge of Python and/or C++ and willingness to learn other languages as needed.
Expert-level knowledge of multiple modern AI/ML, vision, NLP, bioinformatics and/or mathematical or computational libraries.
Familiarity with software containerization technologies such as Docker and Singularity.
Familiarity with RESTful web service principles and development.
Familiarity with SQL and relational database principles and development.
Fluency in the Linux operating system and related tools.
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.
Familiarity with cloud development and deployment.
Demonstrated leadership and self-direction.
Willingness to teach others both informally and in short course format.
Willingness to continually learn new tools and techniques as needed.
Excellent verbal and written communication.
Masters in a quantitative discipline, such as Computer Science, Engineering, Physics or Bioinformatics with strong scientific computing and/or mathematics background.
Three (3) years of experience working in software development in large projects.
Three (3) years of experience in development and application of AI/ML (developing, training and applying state of the art models in practical scientific applications aligned with DSAI domains) OR Data science (modeling, transforming, applying ETL pipelines, and similar operations to complex data sets at scale).

Nice To Haves

PhD in a quantitative discipline (highly preferred).
Five (5) years’ experience as above in either AI/ML or data science concentration.
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, and heath record data.
Experience in CUDA GPU programming.
Experience authoring open-source Python packages in PyPI.
Experience in open-source project governance.
Experience in open-source community adoption initiatives.

Responsibilities

Work collaboratively in a team with other RSEs and scientists.
Participate in ground-breaking research projects requiring advanced software solutions and expertise in software engineering not commonly found in scientific collaborations.
Create AI/ML solutions using the latest deep learning libraries trained on state-of-the-art hardware.
Analyze massive data sets either in the cloud or on premises.
Create novel data science techniques and software pipelines for processing real-time high-frequency data.
Design complex database models for storing and disseminating scientific data sets.
Engage deeply in projects, potentially leading to co-authorship on scientific publications, or provide casual consulting.
Develop software solutions from scratch or refactor existing solutions to conform to industry standards (quality, efficiency, reusability, robustness, portability, documentation, etc.).
Translate individual project efforts into frameworks and template patterns for sustainable scientific infrastructure.
Develop software to implement novel scientific research algorithms.
Create and run data processing workflows utilizing on-premise or cloud-based computing infrastructure.
Develop data models.
Co-author scientific publications describing software and/or other contributions.
Translate recurring themes from specific projects into frameworks and template patterns for sustainable scientific infrastructure.
Lead and participate in service activities, including providing guidance to faculty, staff, and students on AI, data science, and software engineering.
Develop and deliver presentations and short courses.
Attend conferences and workshops.
Conduct code quality reviews.
Participate in hiring.
Perform other activities as needed.