Research Software Engineer - Robotics & Simulation (Data Science and AI Institute)

Johns Hopkins University•Baltimore, MD

4d•Hybrid

About The Position

The Johns Hopkins Data Science and AI Institute (DSAI) is seeking a Research Software Engineer - Robotics & Simulation with a 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.

Requirements

Expert-level knowledge of Python (preferred) 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 and in large projects and three (3) year's 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).
Exceptional ML skills and experience.
Experience with Nvidia ML software ecosystem.
Experience with Nvidia Issac robotics simulation and learning platform
Experience with Fluid dynamics and biomechanical simulations.
Experience with Optimization and GPU acceleration of simulations.
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.
Participates in ground-breaking research projects that need advanced software solutions requiring expertise in software engineering not commonly found in scientific collaborations.
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.).
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-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 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.
Developing and delivering presentations and short courses.
Attending conferences and workshops.
Code quality reviews.
Hiring.
Other activities as needed.