Data Science Workflows Architect

Lawrence Berkeley National Laboratory•Berkeley, CA

13h•Hybrid

About The Position

Lawrence Berkeley National Laboratory is hiring a Data Science Workflows Architect within the NERSC division. The National Energy Research Scientific Computing Center (NERSC) is seeking an engineer with experience in complex scientific workflows to join our team to help scientists take advantage of the most powerful computers in the U.S. As a member working at NERSC in the Data Science Engagement group, you will leverage cutting-edge technologies to advance the scientific mission of the Department of Energy. More than ever, scientific discovery transforms our world. NERSC is at the forefront, operating some of the world’s largest supercomputers for thousands of researchers who use computational power to solve society’s most challenging problems. These researchers are increasingly leveraging the combined power of large-scale data analysis, simulation, and machine learning, integrating resources across multiple facilities. NERSC's next major supercomputer, Doudna, will combine next generation GPUs, networking and storage coupled with advanced capabilities to support future scientific workflows. NERSC is also a major partner in key DOE initiatives to develop new capabilities for scientific innovation, including Integrated Research Infrastructure and the American Science Cloud. In this role, you will be responsible for supporting our user community to adapt and optimize their workflows for High Performance Computing (HPC) systems. You will be part of multidisciplinary and cross-institution scientific projects, working directly with scientists and instruments from around the world. The selected candidate(s) will be hired at the Computer Systems Engineer 3 or 4 (CSE3 or CSE4) depending on their level skills and experience.

Requirements

Typically requires a minimum of 8 years of related experience with a Bachelor’s degree; or 6 years and a Master’s degree; or equivalent experience.
Wide-ranging expertise in the areas of scientific computational workflows, workflow tools, and/or High-Performance Computing.
Software engineering experience, including version control, testing, debugging, and CI/CD.
Programming background in languages including (any one of) C/C++, Python, Julia, Rust, Go, and shell scripting.
Prior experience managing computational needs and resources, either as a user or as an administrator.
Excellent communication and interpersonal skills, able to express yourself clearly in both written (e.g. conference papers, technical papers, documentation, email) and oral (e.g. via zoom and in person) communication.
Demonstrated ability to work effectively as part of a cross-disciplinary team.
Ability to troubleshoot and solve problems of diverse scope where analysis of data requires evaluation of identifiable factors.
Ability to resolve complex issues in creative and effective ways.
Ability to network and collaborate with key contacts outside one’s own area of expertise.
Typically requires a minimum of 12 years of related experience with a Bachelor’s degree; or 8 years and a Master’s degree; or equivalent experience.
Broad expertise and/or unique knowledge in the areas of complex scientific workflows, workflow tools, and/or High-Performance Computing.
Ability to work on and resolve significant and unique issues where analysis of situations or data requires an evaluation of intangibles.
Ability to exercise independent judgment in methods, techniques and evaluation criteria for obtaining results.

Nice To Haves

Advanced Degree (Masters or Ph.D) in a Scientific Domain
Experience working with experiments that have real-time and/or large-scale computing requirements.
Experience with HPC systems, including use of a batch scheduling system.
Experience implementing and/or integrating computational workflow tools to support scientific research.
Understanding of and experience with data analysis and/or AI tools and platforms.
Experience deploying and utilizing Jupyter or other interfaces for interactive data exploration.
Experience with cloud technologies, software, interfaces/APIs and containers.
Experience with (and enthusiasm for) modern computing architectures including GPUs and other Accelerators.
Experience in working on interdisciplinary projects involving multiple scientific and technical teams and institutions.

Responsibilities

Work with domain scientists to integrate HPC resources at NERSC into their workflows.
Collaborate with other teams at NERSC to ensure next generation NERSC systems such as the new supercomputer Doudna can be harnessed by the NERSC user community.
Collaborate with teams across the Department of Energy to support Integrated Research Infrastructure and the emerging American Science Cloud.
Optimize the user environment on NERSC supercomputers to maximize scientific productivity for the user community. This includes installing, maintaining and documenting a productive and performant set of tools (e.g. Jupyter, Podman, Julia or Python), engaging with the developer and user community, helping to optimize systems to meet user needs, and monitoring system performance from an application perspective.
Develop and support services in data management, data movement, agentic AI, and workflow orchestration.
Educate and train users by creating content for the NERSC website with online tutorials and documentation, giving presentations, and attending conferences. Communicate with users about new opportunities and capabilities in software and systems and advise them in effectively transitioning to new technologies.
Communicate clearly to both domain scientists and computer systems engineers, explaining the subtleties of using an HPC system and translating scientific requirements into computing needs.
Work on and resolve complex issues where analysis of situations or data requires an in-depth evaluation of variable factors.
Exercise judgment in selecting methods, techniques and evaluation criteria for obtaining results.
Determine methods and procedures on new assignments and may coordinate activities of other personnel.
Network with key contacts outside one’s own area of expertise.
Work on and resolve significant and unique issues where analysis of situations or data requires an evaluation of intangibles.
Exercise independent judgment in methods, techniques and evaluation criteria for obtaining results.