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

The Johns Hopkins Data Science and AI Institute (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. DSAI seeks a Research Software Engineer – Image Analysis & Microscopy with strong academic and industry background focused on designing and building software for state-of-the-art AI and data science applications across diverse scientific 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 disciplines.The DSAI engineers will be at the forefront of modern data intensive science, where professionally developed software is rapidly becoming a key ingredient for success. The DSAI initiative includes the build-out of a substantive and professional-scale software engineering capability. Specific Duties & Responsibilities Work collaboratively in a team with other RSEs and scientists.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. Involve analysis of massive data sets either in the cloud or on premises. 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. Require deep engagement, possibly leading to co-authorship on scientific publications, while others may involve a more casual consulting engagement. 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. 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 includingProviding guidance to faculty, staff, and students on AI, data science and software engineering. Developing and delivering presentations and short courses. Attending conferences and workshops. Code quality reviews. Hiring. Other activities as needed. Special Knowledge, Skills, and Abilities required 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.Minimum QualificationsMasters 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 and three (3) years’ experience in development and application ofAI/ML: developing, training and applying state of the art models in practical scientific applications aligned with DSAI domains, orData science: modeling, transforming, applying ETL pipelines, and similar operations to complex data sets at scale.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 QualificationsPhD 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.Classified Title: Scientific Software Engineer Job Posting Title (Working Title): Research Software Engineer – Image Analysis & Microscopy (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