Hardware Systems Engineer – AI Hardware

Cloudflare

. Contribute to technical requirements and end-to-end delivery of AI infrastructure covering compute, networking, storage, power, and cooling .

Posted 11/4/2026full-timeLisbon • PortugalMid-LevelSenior💰 €54,000 - €68,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in AI infrastructure design and qualification, with a strong focus on GPU systems, hardware platforms, and performance benchmarking. Proficient in scripting and automation to enhance qualification workflows and technical project management.

Highest-signal resume keywords

AI Infrastructure EngineeringGPU System Design and ValidationScripting and Automation with PythonPerformance Benchmarking and TestingTechnical Communication and Documentation

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

AI Hardware PlatformsGPU SystemsNCCL TestsNVLink DebuggingPCIe Lane TroubleshootingAI/ML FrameworksRack-Scale SystemsPerformance TestingFirmware UpdatesAI Workload Patterns

Soft Skills

Organizational SkillsSelf-MotivatedCross-Functional CollaborationWritten Communication

Tools & Technologies

PrometheusGrafanaGitTeamCityJira

Industry Keywords

AI InfrastructureODM/OEM PlatformsCloud Infrastructure DesignOpen Compute Hardware ProgramsHyperscaler Design

Tech Stack

Tools & technologies

CloudGrafanaLinuxNode.jsPrometheusPythonGo

About the role

Key responsibilities & impact

Contribute to technical requirements and end-to-end delivery of AI infrastructure covering compute, networking, storage, power, and cooling
Evaluate, select, and qualify AI hardware platforms such as GPUs, custom ASICs, SmartNICs, and storage
Design and validate rack-scale GPU systems, including multi-node topologies, NVLink, InfiniBand, RoCE, power distribution, and thermal management
Run hands-on qualification through benchmarking, stress testing, failure-mode analysis, and vendor root-cause analysis
Assess and qualify ODM/OEM server platforms and engage vendors throughout the technical qualification cycle
Translate AI product and workload requirements into infrastructure specifications
Identify infrastructure-stack gaps and drive solutions across hardware, firmware, software, and operations
Track developments in AI hardware and provide recommendations
Build tooling and automation for qualification workflows, performance analysis, and operational visibility
Communicate results and updates through blog posts and internal talks
Collaborate with Engineering, Product, Performance, and Hardware Sourcing teams to design, qualify, and maintain Cloudflare’s worldwide GPU-server fleet

Requirements

What you’ll need

Bachelor’s, Master’s, or PhD degree in Computer Engineering, Electrical Engineering, or Computer Science
Experience in AI infrastructure, systems engineering, or hardware platform engineering
First-principles understanding of AI accelerator architecture, including compute pipelines, memory hierarchy, and interconnect topology
Hands-on experience qualifying AI server platforms, including NCCL tests, collective-operations profiling, NVLink debugging, and PCIe lane troubleshooting
Understanding of rack-scale systems, high-density power, liquid cooling, high-speed networking infrastructure, and/or physical-layer constraints
Experience with AI/ML frameworks such as vllm and sglang at the systems level
Familiarity with AI server form factors including HGX, OAC, OAM, UBB, and open rack standards
Scripting and automation skills using Python, Bash, Go, and Linux system administration
Strong written communication and ability to write technical specifications and vendor requirements documents
Knowledge of performance benchmarking and hardware performance testing, measurement, and/or simulation
Knowledge of Redfish, IPMI, server remote-management protocols, and firmware updates
Ability to use AI tools and automation to accelerate qualification workflows
Organizational skills and interest in managing cross-functional technical projects
Ability to work as a self-motivated engineer in ambiguous environments
Experience with major AI server ODMs/OEMs and qualification/customization processes is a bonus
Exposure to hyperscaler or cloud infrastructure design is a bonus
Experience with RDMA networking, RoCEv2 congestion control, or InfiniBand fabric management is a bonus
Understanding of AI workload patterns, including LLM training versus inference and model/tensor/pipeline parallelism, is a bonus
Familiarity with open compute hardware programs or OCP specifications is a bonus
Experience with hardware/software codesign performance optimization, kernel development, compiler optimization, or driver development is a bonus
Experience with Prometheus, Grafana, Git, TeamCity, and Jira is a bonus
Knowledge of debugging server hardware faults and working with sourcing teams and vendors is a bonus

Benefits

Comp & perks

Equity plan participation
Medical/Rx Insurance
Dental Insurance
Vision Insurance
Flexible Spending Accounts
Commuter Spending Accounts
Fertility & Family Forming Benefits
On-demand mental health support and Employee Assistance Program
Global Travel Medical Insurance
Short and Long Term Disability Insurance
Life & Accident Insurance
401(k) Retirement Savings Plan
Employee Stock Participation Plan
Flexible paid time off covering vacation and sick leave
Leave programs, including parental, pregnancy health, medical, and bereavement leave