Research Software Engineer

The Johns Hopkins Data Science and AI Institute (DSAI) is 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. 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 multiple Research Software Engineers with strong academic backgrounds and relevant experience in industry focused on designing and building state-of-the art AI models, data science techniques and applications of each to 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.

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, and a dramatic increase in infrastructure, both in hardware and in personnel. JHU has long been a world leader in the broader domains of medicine and public health as well as a wide range of science and engineering fields. This combined with our ethos of building out capabilities to have demonstrable global impact (e.g., JHUs Coronavirus Resource Center the award-winning global resource for real-time data and analysis for COVID-19) and other unique large scientific data sets, like the archives for the Sloan Digital Sky Survey and several simulations, will be key leverage points that will make the DSAI successful.

Specific Duties & Responsibilities

• The successful candidates will participate 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 the individual projects into frameworks and template patterns for sustainable scientific infrastructure benefiting future projects.

Special knowledge, skills, and abilities

• Expert-level knowledge of the Python programming language.
• Expert-level knowledge of multiple modern AI/ML, vision, NLP, bioinformatics and/or mathematical or computational libraries.
• Familiarity with or willingness to learn C++ or other languages may be needed.
• 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