Senior HPC Architect

Oak Ridge National Laboratory•Oak Ridge, TN

1d•Onsite

About The Position

As a Senior HPC Architect at Oak Ridge National Laboratory (ORNL), you will lead the architecture, design, and evolution of high-performance computing platforms supporting both open research and classified computing missions. This role is responsible for translating mission and scientific requirements into scalable, reliable, and secure HPC architectures—spanning compute (CPU/GPU), high-speed interconnects, storage and parallel file systems, cluster management, and user-facing platform services. In practice, the Senior HPC Architect drives end-to-end technical direction for HPC environments: developing reference architectures, guiding system design decisions and technology selection, defining performance and capacity models, and ensuring operational excellence through standardization, automation, and observability. The position partners closely with cybersecurity and compliance stakeholders to deliver secure-by-design infrastructure aligned with regulatory requirements and authorization processes (e.g., ATO, NIST controls, STIG implementation). This role also provides technical leadership across teams—mentoring engineers, leading architecture reviews, and coordinating delivery across infrastructure, security, and scientific programs. Success requires deep expertise in modern HPC system architecture, GPU-centric platforms, cluster scheduling, Linux at scale, and rigorous performance engineering, with the ability to operate effectively in regulated environments.

Requirements

BS degree in Computer Science, Engineering, or a related field and a minimum of 8+ years of relevant experience (or equivalent combination of education and experience).
8+ years of experience in HPC engineering with demonstrated strength in system architecture, cluster operations, parallel computing environments, and performance optimization.
Demonstrated experience working in high-security and/or regulated environments
Strong experience with HPC cluster management and scheduling
Experience with HPC performance monitoring and benchmarking using tools such as Grafana, Nagios, Ganglia (or equivalent).
Ability to lead technical initiatives, write clear technical documentation, and communicate effectively with both engineering and non-engineering stakeholders.

Nice To Haves

Familiarity with parallel file systems / advanced storage
Experience with containerization and HPC-adjacent platforms (e.g., Docker, Kubernetes, Kubeflow) in a way that complements scheduler-based HPC usage.
Experience with virtualization platforms (e.g., VMware) in support of HPC infrastructure services.
Strong Infrastructure-as-Code background (e.g., Ansible, plus cloud/IaC such as Terraform/Packer where relevant).
Experience supporting scientific software development and deployment and research user workflows.
Preferred: experience with geospatial data workflows, including large geospatial/raster/vector datasets, spatial ETL pipelines, and performance considerations for geospatial analytics at scale (e.g., tiling/partitioning strategies, I/O patterns, reproducibility, and access controls for sensitive geospatial data).
Strong leadership, mentoring, and cross-team coordination skills; ability to manage multiple priorities in fast-paced, high-consequence environments.

Responsibilities

Lead the design and deployment of HPC systems to meet performance, reliability, and security requirements for research and/or classified computing environments.
Define and maintain reference architectures for compute, network, storage, and platform services, including lifecycle planning and roadmap development.
Produce and maintain technical documentation: architecture diagrams, configuration standards, operational procedures, and engineering decision records.
Guide system and accelerator architecture decisions with a strong grasp of how GPU/accelerator architecture impacts datacenter and AI/HPC workloads (including LLM-adjacent workloads where applicable).
Identify automation targets and lead adoption of infrastructure-as-code and configuration management (e.g., Ansible, Puppet, Chef, Kickstart, Satellite).
Build standardized, repeatable deployment workflows; contribute to internal platform tools and codebases where appropriate (e.g., Python-based automation).
Where applicable, architect and support container and platform capabilities (e.g., Docker/Kubernetes) to enable reproducible scientific workflows and service deployment.
Lead HPC-related projects from planning through implementation and steady-state operations; manage technical risks, dependencies, and milestones.
Partner with scientists, researchers, and mission stakeholders to translate workflow requirements into platform capabilities.
Mentor junior engineers; create knowledge-sharing practices, documentation, and engineering standards.