Software and Infrastructure Engineer

The Transmitter•New York, NY

8d•$150,000 - $175,000•Onsite

About The Position

The Scientific Computing Core (SCC) is the technical backbone of the Flatiron Institute. SCC develops, deploys and maintains computational infrastructure from supercomputers to desktop workstations and provides resources, services, and expertise to the Institute's research community. We are seeking a Software and Infrastructure Engineer who is passionate about technology, innovation, and science to join the team in maintaining and growing Flatiron's research computing resources. They will work in the deployment, operation and maintenance of the Flatiron Institute’s Scientific computing service infrastructure both on prem and in the cloud, and support scientific applications in the Flatiron Institute scientific computing centers. This is a full-time position based in Simons Foundation’s offices in New York City. Visit the Simons Foundation career page to learn more.

Requirements

Bachelor's degree in computer science, engineering, computational science, mathematics, physics, neuroscience, biology or a related technical or scientific field, or equivalent professional experience.
2 to 5 years of professional software engineering, cloud engineering, research computing, scientific computing or related experience. Relevant research and substantial technical project experience may also be considered.
Demonstrated experience developing and maintaining production software or cloud-based services.
Strong programming experience in Python and experience with one or more additional languages such as Go, Java, C++, C# or similar.
Practical experience working in Linux environments.
Experience with cloud computing platforms such as AWS, Google Cloud Platform or Microsoft Azure.
Experience with containers and containerized application environments, including Docker; familiarity with Kubernetes or similar orchestration technologies is highly desirable.
Experience diagnosing software and infrastructure problems across applications, services, operating systems, networks or computing environments.
Experience with performance analysis, resource utilization, reliability or optimization of production or computational workloads.
Experience developing automation, APIs, services or tools that improve operational efficiency and reduce manual processes.
Strong software engineering skills, including experience developing maintainable, tested and documented software.
Strong working knowledge of Linux, cloud computing and containerized environments.
Familiarity with distributed systems, APIs, databases and modern software development practices, including Git and continuous integration.
Ability to troubleshoot application and infrastructure issues across multiple services and identify performance or resource bottlenecks.
Experience with automation, scripting and tools that improve deployment, monitoring, configuration and operational efficiency.
Understanding of cloud resource management, security and cost-conscious use of compute, storage and other infrastructure.
Ability to work collaboratively across software engineering, infrastructure and scientific teams.
Interest in working directly with scientists and supporting computational research.
Ability to learn unfamiliar scientific software, data formats and research workflows and develop sufficient domain understanding to diagnose technical problems effectively.
Translate scientific or user requirements into practical software and infrastructure solutions.
Interest in supporting neuroscience research and developing familiarity with the applications, data and computational workflows used by neuroscience researchers.

Nice To Haves

Experience with Kubernetes or similar orchestration technologies is highly desirable.
Experience with scientific computing, computational research, biological data, neuroscience, machine learning or another data-intensive scientific domain is desirable but not required.
Experience applying computing to scientific, research or data-intensive problems is desirable.
Experience building or supporting reproducible computational or data-processing workflows is desirable.
Experience working with computational biology, bioinformatics, neuroscience, imaging or other scientific datasets is a plus.
GPU-based computing and familiarity with CUDA or GPU-enabled scientific or machine-learning applications.
Running, debugging or optimizing computational workloads subject to GPU memory or other resource constraints.
Machine-learning frameworks such as PyTorch or similar tools.
Deployment or operation of AI/ML models and associated data-processing workflows.
Distributed or large-scale computing environments.
Performance testing and optimization of computational workloads.

Responsibilities

Develop, deploy and maintain software, services and tools that support scientific computing and data-intensive research.
Work with scientists to translate research requirements into practical software, cloud and infrastructure solutions.
Support and troubleshoot scientific applications and workflows across Linux, cloud, containerized and distributed computing environments.
Diagnose performance, reliability and resource-utilization issues and optimize computational workloads.
Develop automation, APIs and operational tools that improve deployment, monitoring and efficiency.
Support reproducible scientific workflows involving large datasets, machine learning, GPUs or other advanced computing technologies.
Evaluate and adopt appropriate emerging technologies for scientific applications.
Collaborate across scientific, software engineering and infrastructure teams and contribute to documentation and knowledge sharing.
Perform other duties or tasks as assigned or required.