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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Research Software Engineer - Robotics & Simulation (Data Science and AIInstitute) - **Company:** JOHNS HOPKINS - **Location:** Baltimore, MD, United States - **Experience:** Experienced - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Build Automation, Bioinformatics, C++ (Programming Language), Cloud Computing, Cloud Engineering, Software Quality, Code Review, Nvidia CUDA, Continuous Delivery, Continuous Integration, Extract Transform Load (ETL), Relational Databases, Database Models, Linux, Python (Programming Language), Machine Learning, Open Source Technology, Scientific Computating, Software Construction, Software Engineering, Software Systems, SQL Databases, Test-Driven Development (TDD), Large Language Models, Deep Learning, Gpu Programming, Git, Containerization, Information Technology, Data Analytics, Restful APIs, Software Version Control, Data Pipelines, Docker - **Published:** October 4, 2026 - **Apply:** https://www.juju.com/job/21_01a0b760-94ac-75ce-8898-f892cdf3bcb7 ## About the Role * 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. Minimum Qualifications * 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. 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 (highly preferred). * Exceptional ML skills and experience. * Experience with, * Nvidia ML software ecosystem. * Nvidia Issac robotics simulation and learning platform * Fluid dynamics and biomechanical simulations. * 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. ## Description * 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-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. ## Related Videos - [Docker network without Docker](https://www.wearedevelopers.com/videos/1418-docker-network-without-docker) - [How a Small Team Shrank a Microsoft Monorepo by 94%](https://www.wearedevelopers.com/videos/1236-how-a-small-team-shrank-a-microsoft-monorepo-by-94) - [Docker Compose: Rediscovered](https://www.wearedevelopers.com/videos/1978-docker-compose-rediscovered) - [Enhancing AI-based Robotics with Simulation Workflows](https://www.wearedevelopers.com/videos/472-enhancing-ai-based-robotics-with-simulation-workflows) - [Docker exec without Docker](https://www.wearedevelopers.com/videos/1094-docker-exec-without-docker) - [Docker build without Docker](https://www.wearedevelopers.com/videos/100114-docker-build-without-docker) ## Related Articles - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [Got AI ideas but no money? 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