C

Cloudflare

Secures websites and runs apps on its network

Hardware Systems Engineer - AI Hardware

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

About the Job

Requirements

  • Experience in AI infrastructure, systems engineering, or hardware platform engineering.
  • Understanding of AI accelerator architecture, including compute pipelines, memory hierarchy, interconnect topology, and workload-to-hardware mapping.
  • Hands-on experience qualifying AI server platforms, including NCCL testing, profiling collective operations, debugging NVLink errors, and investigating PCIe lane misconfigurations.
  • Understanding of rack-scale systems, including high-density power, busbars, PDUs, liquid cooling, high-speed networking infrastructure, and physical-layer constraints.
  • Systems-level experience with AI/ML frameworks such as vLLM or SGLang, including their hardware requirements and bottlenecks.
  • Familiarity with AI server form factors such as HGX, OAC, OAM, UBB, and open rack standards.
  • Scripting and automation skills using Python, Bash, Go, and Linux system administration.
  • Ability to write technical specifications and vendor requirements documents.
  • Knowledge of performance benchmarking and experience testing, measuring, or simulating hardware performance.
  • Knowledge of Redfish, IPMI, server remote-management protocols, and firmware updates.
  • Ability to use AI tools and automation to accelerate the qualification lifecycle and improve efficiency and quality.
  • Organizational skills and interest in managing cross-functional technical projects and aligning architectural designs with delivery schedules.
  • Ability to drive initiatives independently in ambiguous environments.
  • Ability to make defensible technical decisions with incomplete information and communicate the reasoning clearly.
  • Experience with major AI server original design manufacturers (ODMs) and original equipment manufacturers (OEMs), including their qualification and customization processes.
  • Exposure to hyperscaler or cloud infrastructure design, including GPU capacity provisioning, allocation, and operation at scale.
  • Experience with RDMA networking, RoCEv2 congestion control, or InfiniBand fabric management.
  • Understanding of AI workload patterns, including large-language-model training versus inference, model/tensor/pipeline parallelism, and memory and bandwidth implications.
  • Familiarity with open-compute hardware programs or OCP specifications.
  • Experience optimizing performance through hardware/software codesign.
  • Experience with kernel development, compiler optimization, or driver development.
  • Experience with observability and monitoring tools such as Prometheus and Grafana, including trend monitoring.
  • Experience with software development tools and processes such as Git, TeamCity, and Jira.
  • Experience in a hyperscale cloud infrastructure role.
  • Knowledge of server-hardware fault debugging and ability to work with sourcing teams and vendors to improve quality.
  • Ability to rack servers in a lab and write technical requirements documents.

Responsibilities

  • Contribute to technical requirements and end-to-end delivery of AI infrastructure spanning compute, networking, storage, power, and cooling.
  • Evaluate, select, and qualify AI hardware platforms, including GPUs, custom ASICs, SmartNICs, and storage, based on workload requirements and first-principles analysis.
  • Design and validate rack-scale GPU systems, including multi-node topologies, high-speed interconnects, power distribution, and thermal management.
  • Perform hands-on qualification through benchmarking, stress testing, failure-mode analysis, and vendor root-cause analysis.
  • Assess and qualify ODM/OEM server platforms, engaging vendors throughout the technical qualification cycle.
  • Translate AI product and workload requirements into infrastructure specifications.
  • Identify gaps in the infrastructure stack and drive solutions across hardware, firmware, software, and operations.
  • Track developments in AI hardware, including silicon, interconnects, and form factors, and provide recommendations.
  • Build tooling and automation for qualification workflows, performance analysis, and ongoing operational visibility.
  • Communicate results and updates through blog posts and internal talks.

Desired Qualifications

  • Experience with AI infrastructure, systems engineering, or hardware platform engineering.
  • First-principles understanding of AI accelerator architecture, including compute pipelines, memory hierarchy, interconnect topology, and workload-to-hardware mapping.
  • Hands-on AI server platform qualification, including NCCL tests, collective-operation profiling, NVLink debugging, and PCIe lane troubleshooting.
  • Understanding of rack-scale power, cooling, networking, and physical-layer constraints.
  • Systems-level experience with vLLM or SGLang.
  • Familiarity with AI server form factors and open rack standards.
  • Scripting and automation with Python, Bash, Go, and Linux system administration.
  • Strong written communication for technical specifications and vendor requirements documents.
  • Performance benchmarking and hardware testing, measurement, or simulation experience.
  • Knowledge of Redfish, IPMI, remote server management protocols, and firmware updates.
  • Ability to use AI tools and automation to accelerate qualification.
  • Experience with AI server ODMs/OEMs and qualification or customization processes.
  • Exposure to hyperscaler or cloud infrastructure design and GPU capacity operations.
  • Experience with RDMA, RoCEv2 congestion control, or InfiniBand fabric management.
  • Understanding of AI workload patterns, parallelism, and memory/bandwidth requirements.
  • Familiarity with open-compute hardware programs or OCP specifications.
  • Hardware/software codesign for performance optimization.
  • Kernel development, compiler optimization, or driver development experience.
  • Experience with Prometheus, Grafana, and monitoring trends over time.
  • Experience with Git, TeamCity, and Jira.
  • Experience in hyperscale cloud infrastructure.
  • Server hardware fault-debugging knowledge and vendor/sourcing quality engagement.

About the Company

Cloudflare

Secures websites and runs apps on its network

Cloudflare operates a network that protects websites, delivers online content and runs applications closer to their users. Businesses connect websites and internal systems to its services to filter malicious traffic, control access and improve performance, with security and computing running on shared infrastructure. Developers combine application code, object storage and databases with the network used for content delivery and traffic protection. Cloudflare earns revenue from subscriptions and usage charges, with free plans for basic features and negotiated contracts for larger organizations. Cloudflare was founded in 2009 and is headquartered in San Francisco, California.

Company size
1,001-5,000
Company stage
IPO
Headquarters
San Francisco, CA
Founded
2009

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