Scientific Software Engineer; Data Science & AI Institute

Scientific Software Engineer (Data Science & AI Institute)

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The Johns Hopkins Data Science and AI Institute (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, Safety & Assurance, and Discovery & Inquiry.

DSAI seeks a Scientific Software Engineer with a strong academic background and relevant experience in industry. The successful candidates will work at the cutting edge of modern science within the new Scientific Software Engineering Center (SSEC) at Johns Hopkins University (JHU). The Center is part of the Virtual Institute for Scientific Software (VISS), launched by Schmidt Futures, a philanthropic initiative founded by Eric and Wendy Schmidt.

VISS will address the growing demand for high‑quality professional software engineers who can build dynamic, scalable, open software to facilitate accelerated scientific discovery across fields. The SSEC will be hosted by the Institute of Data Intensive Engineering and Science (IDIES) at JHU within DSAI, a new 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.

The SSEC engineers will be at the forefront of modern data‑intensive science, where high‑level software is rapidly becoming the key ingredient for success. The DSAI initiative includes the build‑out of a substantive and professional‑scale software engineering capability, and a dramatic increase in infrastructure, both in hardware and in personnel.

• The successful candidates will be given a choice of ground‑breaking research projects that need advanced software solutions requiring expertise in software engineering not commonly found in scientific collaborations.
• Projects may require the creation of AI/ML solutions using the latest DNN 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 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, reusability, robustness, portability, documentation, etc.).
• It is a high‑level goal of the SSEC to translate the efforts for the individual projects into frameworks and template patterns for sustainable scientific infrastructure benefiting future projects.

• Master’s in a Quantitative Discipline, e.g., Computer Science, Engineering, Astrophysics, Bioinformatics with strong scientific computing and/or mathematics background.
• Three (3) years or more experience working in software development and/or data science in large projects in industry.
• 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.

• PhD in a quantitative discipline.
• Five years or more experience working in software development and/or data science in industry.
• Experience with articulating and translating business/application questions and translating these into software and statistical techniques to arrive at an answer using available data.
• Demonstrated leadership and self‑direction.
• Willingness to both teach others and learn new techniques.
• Demons…