What You'll Be Doing

Contribute to the technical requirements and end-to-end delivery of AI infrastructure including compute, networking, storage, power, and cooling
Evaluate, select, and qualify AI hardware platforms (GPUs, custom ASICs, SmartNICs, storage) based on workload requirements and analysis
Design and validate rack-scale GPU systems with multi-node topologies, high-speed interconnects, power distribution, and thermal management
Perform hands-on qualification tasks such as benchmarking, stress testing, failure mode analysis, and vendor root cause analysis
Assess and qualify ODM/OEM server platforms; engage with vendors and manage the full technical qualification lifecycle
Translate AI product and workload requirements into precise infrastructure specifications
Identify infrastructure gaps and drive solutions across hardware, firmware, software, and operations
Keep up to date with the AI hardware landscape and provide informed recommendations
Build tooling and automation for qualification workflows, performance analysis, and operational visibility
Communicate results and updates through blog posts and internal presentations

Requirements

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, memory hierarchies, and interconnect topologies
Hands-on experience qualifying AI server platforms, including benchmarking and debugging hardware errors
Knowledge of rack-scale systems including high-density power, liquid cooling, and high-speed networking infrastructure
Familiarity with AI/ML frameworks at the systems level and their hardware demands
Experience with AI server form factors and their tradeoffs
Scripting and automation skills in Python, Bash, Go, and Linux system administration
Strong written communication skills for technical specifications and vendor documents
Knowledge of performance benchmarking and thorough experimentation methodologies
Experience with Redfish, IPMI, server remote management protocols, and firmware updates
Ability to harness AI tools and automation to accelerate qualification processes
Organizational skills to manage cross-functional technical projects
Self-motivated with the ability to work independently in ambiguous environments

Experience with major AI server ODMs/OEMs and qualification/customization processes
Exposure to hyperscaler or cloud infrastructure design and GPU capacity management at scale
Experience with RDMA networking, RoCEv2 congestion control, or InfiniBand fabric management
Understanding of AI workload patterns like LLM training vs inference and parallelism methods
Familiarity with open compute hardware programs and OCP specifications
Experience in hardware/software co-design for performance optimization
Kernel development, compiler optimization, or driver development experience
Knowledge of observability and monitoring tools such as Prometheus and Grafana
Experience with software development tools and processes like Git, TeamCity, and Jira
Background in hyperscale cloud infrastructure roles
Ability to debug server hardware faults and coordinate with sourcing and vendors