Lead Test Engineer, Compute Server & Storage - AI Data Center

General Overview

Functional Area:  Engineering        
Career Stream:  Design - Software Engineering
SAP Short Name:  LEN-ENG-DSE
Job Level:  Level 08
IC/MGR:  Individual Contributor        
Direct/Indirect Indicator:  Indirect

Summary

The Lead Storage and Server Test Engineer will play a pivotal role in the design, development, and execution of comprehensive test strategies for our AI data center's storage and server infrastructure. This leadership position requires deep expertise in enterprise storage systems, server architectures, networking, and a strong understanding of the unique performance and reliability demands of AI/ML workloads. The ideal candidate will be a hands-on technical leader, capable of mentoring junior engineers, driving test automation, and collaborating across engineering teams to deliver robust and high-performing solutions.

Required Qualifications

• Bachelor's or Master's degree in Computer Science, Electrical Engineering, or a related technical field.

• 5+ years of experience in hardware and/or software testing, with at least 5 years focused on enterprise-level storage and server systems.

• Proven experience in a lead or senior technical role, mentoring and guiding other engineers.

• Deep expertise in various storage technologies including NVMe, SAS/SATA SSDs/HDDs, RAID, distributed file systems (e.g., Ceph, Lustre, GPFS), SAN, and NAS.

• Strong understanding of server architectures (x86, ARM, GPU servers), CPU/memory subsystems, PCIe, and power management.

• Proficiency in scripting languages (e.g., Python, Bash) for test automation and data analysis.

• Experience with Linux operating systems (e.g., Ubuntu, CentOS, RHEL) and command-line tools.

• Familiarity with networking concepts (Ethernet, TCP/IP, InfiniBand) and network testing methodologies.

• Experience with test methodologies such as performance testing, reliability testing, stress testing, and fault injection.

• Excellent problem-solving, analytical, and debugging skills.

• Strong communication and interpersonal skills, with the ability to collaborate effectively across diverse teams.

Knowledge/Skills/Competencies

Test Strategy & Leadership:

• • • Contribute to the development and implementation of test plans and strategies for storage, server hardware, firmware, and software components in our AI data center.

    • Execute and analyze complex test cases, including functional, performance, reliability, stress, and endurance testing.

    • Learn from and collaborate with senior engineers, adopting best practices and contributing to team knowledge.

    • Keep up-to-date with industry trends, emerging technologies, and best practices in storage, server, and AI infrastructure testing.

  • Technical Execution & Automation:

    • Design and implement automated test frameworks and scripts using languages like Python, Go, or similar, to improve efficiency and coverage of testing.

    • Conduct in-depth performance analysis and bottleneck identification for and server platforms (e.g., CPU, GPU, memory, PCIe, networking).

    • Troubleshoot complex hardware and software issues, working closely with development and operations teams to identify root causes and propose solutions.

    • Develop and maintain robust testbeds and infrastructure for continuous integration and validation.

    • Utilize and contribute to open-source and commercial test tools relevant to storage and server validation.

  • Collaboration & Communication:

    • Collaborate closely with hardware design, software development, infrastructure, and AI/ML engineering teams to understand requirements and integrate testing throughout the product lifecycle.

    • Communicate test progress, results, and critical issues effectively to stakeholders, including executive leadership.

    • Participate in design reviews, architecture discussions, and product roadmap planning to ensure testability and quality are built in from the start.

  • AI/ML Specific Considerations:

    • Develop specialized test methodologies to validate performance and reliability under heavy AI/ML workloads (e.g., large model training, inference at scale, data ingestion).

    • Assess the impact of storage and server configurations on AI model training times, throughput, and accuracy.

    • Understand and test the interactions between GPU-accelerated computing, high-speed networking, and storage systems.

Preffered Qualifications

• Familiarity with OCP (Open Compute Project)

• Experience with cloud environments (AWS, Azure, GCP) and virtualization technologies.

• Knowledge of containerization technologies (Docker, Kubernetes).

• Familiarity with AI/ML frameworks (e.g., TensorFlow, PyTorch) and their infrastructure requirements.

• Experience with performance profiling tools (e.g., fio, Iometer, Perf, VTune).

• Contributions to open-source projects related to storage, servers, or testing.

• Certifications in relevant technologies (e.g., NetApp, Dell EMC, HPE, NVIDIA).

Typical Experience

• 3 to 8 years

Typical Education

Bachelor degree or consideration of an equivalent combination of education and experience.

Educational Requirements may vary by Geography

Notes

This job description is not intended to be an exhaustive list of all duties and responsibilities of the position. Employees are held accountable for all duties of the job. Job duties and the % of time identified for any function are subject to change at any time.