HPC Systems Architect

What You’ll Do

• Architect and define scalable compute platforms optimized for AI/ML, simulation, and high-throughput workloads.
• Develop compute system standards and design patterns to ensure consistency, performance, and maintainability across infrastructure.
• Evaluate emerging CPU, GPU, and accelerator technologies, owning architectural tradeoff decisions that impact compute density, power, cooling, and total cost.
• Collaborate with product and engineering teams to map workload requirements to compute platform capabilities across bare metal and cloud deployments.
• Experience converting ambiguous business or customer needs into measurable platform requirements, technical specifications, acceptance criteria, and architecture decisions.
• Define compute platform roadmaps and architectural reference designs that guide hardware selection, firmware baselines, rack-level, and cluster design.
• Act as a technical lead during new platform introductions, guiding validation and performance characterization efforts.
• Mentor systems engineers and cross-functional stakeholders on compute performance tuning, sizing, and architectural decisions.

You

• Proven experience (7+ years) architecting large-scale 10k-100k+ GPU HPC or cloud compute platforms.
• Deep knowledge of CPU/GPU architectures, memory hierarchies, and accelerator topologies.
• Experience designing systems around high-bandwidth, low-latency fabrics (NVLink, InfiniBand, and RoCE).
• Strong understanding of system performance tuning, resource scheduling, thermal and power optimization, and compute lifecycle management.
• Comfortable working across hardware and software boundaries, especially at the intersection of compute architecture, OS behavior, and orchestration layers.
• Skilled at balancing architectural tradeoffs for density, power efficiency, cooling, and performance.
• Strong analytical and communication skills, with a track record of influencing technical strategy across teams.
• Strong ownership and can do attitude, self-starter who feels comfortable working in ambiguity.

Nice to Have

• Hands-on experience with AI/ML workloads and their compute performance characteristics.
• Familiarity with orchestration tools used in HPC. (Slurm, Kubernetes, etc)
• Experience with virtualization technologies, specifically GPU virtualization.
• Exposure to hardware validation, vendor collaboration, and long-term OEM roadmap alignment.
• Background in compute telemetry, real-time performance profiling, or large-scale A/B infrastructure testing.