Scientific Machine Learning Software Developer

The Johns Hopkins University Applied Physics Laboratory - Laurel, MD

Full-time - Entry Level

Laurel, MD

Professional, Scientific, and Technical Services

About the position

Are you a software developer, excited about developing artificial intelligence (AI) to solve scientific problems? Do you enjoy contributing to critical challenges that require cross-disciplinary approaches? If so, we're looking for someone like you to join our team at APL! We are seeking a Scientific Machine Learning Software Developer for the Complex Systems Group in the Research & Exploratory Development Department to be a part of multidisciplinary teams focused on the research and development of innovative algorithms and prototypes for scientific discovery. Our Complex Systems Group, within the Intelligent Systems Center (jhuapl/isc), develops novel AI methods to accelerate discovery and design for materials science, protein engineering, climate intelligence and earth systems, as well as other physical and biological systems. We strive to foster an environment that welcomes a diversity of backgrounds and professional experience as we make advances in machine learning and artificial intelligence. Scientific machine learning (SciML) is a burgeoning research area seeking to integrate novel machine learning (ML) approaches with existing scientific models, often based on differential equations, to help accelerate scientific discovery. You will work alongside hardworking scientific and engineering teams to build machine learning software addressing scientific challenges such as materials discovery, synthetic biology, chemistry, computational fluid dynamics, and climate and weather modeling. As a Scientific Machine Learning Software Developer, in the Complex Systems Group you will contribute to and/or lead teams in artificial intelligence, including (but not limited to) the development and testing of prototype software implementing innovative algorithms. You will design, develop, and deploy software solutions for a variety of exciting projects addressing scientific discovery. Additionally, you will co-author and present papers on current research and development activities at external venues, and communicate often and effectively with sponsors and APL team members and leadership, by actively participating in the APL data science, analytics, and AI community.

Responsibilities

• Contribute to and/or lead teams in artificial intelligence, including the development and testing of prototype software implementing innovative algorithms.
• Design, develop, and deploy software solutions for a variety of exciting projects addressing scientific discovery.
• Co-author and present papers on current research and development activities at external venues.
• Communicate often and effectively with sponsors and APL team members and leadership.

Requirements

• Bachelor's Degree in a scientific, mathematic, or engineering field.
• At least 1+ years of professional experience.
• Excellent programming skills and fluency in Python.
• Experience programming with common AI libraries, such as scikit-learn, TensorFlow, or PyTorch; and/or neural network architectures such as CNNs, RNNs, autoencoders, transformers, or multilayer perceptrons.
• Willingness to work with and learn from scientific domain experts.
• Excellent interpersonal, organizational, and communication skills.
• Ability to obtain a Top Secret level security clearance.

Nice-to-haves

• Master's Degree in a relevant scientific, mathematic, or engineering field.
• Five or more years of demonstrated experience in developing software for machine learning research.
• Experience with SciML, e.g. physics-informed neural networks (PINNs), graph neural networks (GNNs), or standard software implementations such as DeepXDE and DGL for Python, and NeuralPDE.jl for Julia.
• Courses in scientific areas such as physics, chemistry or biology and related mathematical topics such as differential equations.

Benefits

• Robust education assistance program
• Unparalleled retirement contributions
• Healthy work/life balance