AI Cluster Validation Engineer (Tester & Programmer)

JD:

Key Responsibilities

• AI Cluster Infrastructure Validation: Build, deploy, and maintain hardware and software test environments for AI cluster validation, qualification, and performance benchmarking.
• Execute test plans, reproduce complex issues, collect and analyze logs, and perform first-level root cause analysis across hardware, software, networking, and system components.
• Develop and enhance automated test frameworks, scripts, and tools to improve validation efficiency, coverage, and repeatability.
• Collaborate with software, hardware, networking, and system engineering teams to investigate issues, validate fixes, and improve overall cluster stability, scalability, and performance.
• Conduct functional, performance, stress, and reliability testing for AI/HPC cluster solutions.
• Document test methodologies, configurations, results, troubleshooting procedures, and operational best practices.

Basic Qualifications

• Bachelor's degree in Computer Science, Computer Engineering, Electrical Engineering, or a related technical field.
• 1 years of experience in deploying, testing, validating, or supporting data center hardware and software systems, with expertise in one or more of the following areas:

Server systems, Networking, Storage

• Strong understanding of Linux operating systems, system administration, and troubleshooting.
• Familiarity with data center cluster management platforms, distributed computing environments, and infrastructure validation methodologies.
• Programming or scripting experience with Python, Bash, or similar languages.
• Strong analytical, debugging, problem-solving, and troubleshooting skills.
• Proven ability to quickly learn new technologies and adapt in a fast-paced engineering environment.
• Self-motivated team player with strong communication and collaboration skills.

Preferred Qualifications

• Experience operating, maintaining, or supporting data center, cloud, or laboratory environments.
• Familiarity with AI/HPC clusters, GPU-based systems, and high-speed interconnect technologies such as InfiniBand, RoCE, NVLink, and Ethernet fabrics.
• Experience developing test automation tools, validation frameworks, or CI/CD pipelines.
• Knowledge of cluster orchestration and management technologies, including Kubernetes, Slurm, virtualization platforms, or cloud infrastructure.
• Experience with performance analysis, benchmarking, workload characterization, and system optimization.
• Understanding of storage technologies, distributed file systems, and AI workload deployment environments.