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Lambda

Cloud-based GPU services for AI training

Staff HPC Systems Architect

Hardware Systems IntegrationConfirmed live in the last 24 hours
Full-time

About the Job

Requirements

  • At least 7 years of experience architecting large-scale 10,000–100,000+ GPU HPC or cloud compute platforms.
  • Deep knowledge of CPU and GPU architectures, memory hierarchies, and accelerator topologies.
  • Experience designing systems around high-bandwidth, low-latency fabrics such as NVLink, InfiniBand, and RoCE.
  • Strong understanding of system performance tuning, resource scheduling, thermal and power optimization, and compute lifecycle management.
  • Comfort working across hardware and software boundaries, especially at the intersection of compute architecture, operating system behavior, and orchestration layers.
  • Skill in balancing architectural tradeoffs for density, power efficiency, cooling, and performance.

Responsibilities

  • Architect and define scalable compute platforms optimized for AI and machine learning, 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, and own architectural tradeoff decisions affecting 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.
  • Convert 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 design, 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.

Desired Qualifications

  • Hands-on experience with AI and machine learning workloads and their compute performance characteristics.
  • Familiarity with orchestration tools used in HPC, such as Slurm and Kubernetes.
  • 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.

About the Company

Lambda

Cloud-based GPU services for AI training

Lambda Labs provides cloud-based GPU services for AI training and inference via its AI Developer Cloud, powered by NVIDIA GH200 Grace Hopper hardware to efficiently train large language models and generative AI. Customers access on-demand or reserved GPUs and are billed by the hour (for example, NVIDIA H100 at about $1.99/hour); it also offers Lambda Echelon, where clients own infrastructure but pay a monthly hosting and support fee, and Lambda Stack, a software suite that helps ML teams install and upgrade tools like PyTorch, TensorFlow, CUDA, cuDNN, and NVIDIA drivers. The company differentiates itself through competitive pricing, available high-end hardware, and an integrated ML software stack that streamlines workflows. Its goal is to enable scalable, affordable AI development and deployment by providing flexible GPU access, reliable hosting, and easy software deployment for teams working with large models.

Company size
501-1,000
Company stage
Late Stage VC
Total funding
$9.1B
Headquarters
San Jose, CA
Founded
2012

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