Research Software Engineer – Image Analysis & Microscopy (Data Science and AI Institute)

Johns Hopkins University•Baltimore, MD

5d•Hybrid

About The Position

The Johns Hopkins Data Science and AI Institute (DSAI) is seeking a Research Software Engineer – Image Analysis & Microscopy with a strong academic and industry background. This role focuses on designing and building software for state-of-the-art AI and data science applications across diverse scientific domains. The successful candidate will work collaboratively with DSAI affiliated faculty at Johns Hopkins University (JHU) 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 includes the build-out of 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’ experience working in software development in large projects.
Three (3) years’ 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 with one or more of: Image analysis, Microscopy, Particle tracking, Data management for high-throughput screening.
Experience developing, training, fine-tuning and applying LLMs and/or foundational models.
Experience deploying AI models onto clinical platforms.
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 that need advanced software solutions requiring 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.
Involve analysis of massive data sets either in the cloud or on premises.
Create 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.
Engage deeply in projects, possibly 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 recurring themes from specific projects into frameworks and template patterns for sustainable scientific infrastructure benefiting future projects.
Develop software to implement novel scientific research algorithms.
Create and run data processing workflows utilizing on-premises 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 benefiting future projects.
Lead and participate in service activities, potentially 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.
Perform code quality reviews.
Participate in hiring.
Perform other activities as needed.