About the job
We are seeking a highly talented and experienced Senior AI Fabric Performance Engineer to take on a critical role within our Performance Lab. In this high-impact position, you will drive the performance benchmarking of AI inference, training and storage workloads with focus on our network infrastructure. You will be responsible to generate reports that aid the customers in deployment and marketing team to position the product.
Responsibilities
Benchmarking & Execution: Install, configure, and run industry-standard AI performance benchmarks, with a strong emphasis on MLPerf (Training and Inference) and NCCL tests.
Fabric Optimization: Tune and optimize network parameters, focusing heavily on Ethernet fabric performance, to ensure seamless data flow for distributed AI workloads running on server clusters.
Deep Debugging: Identify, isolate, and troubleshoot complex system performance bottlenecks spanning across the Linux OS, server hardware, and Ethernet switches.
Automation Development: Design, develop, and implement robust performance testing frameworks and automation tools to streamline continuous benchmarking.
Cross-Functional Collaboration: Document test methodologies, communicate performance findings, and provide actionable improvement recommendations to hardware, software, and networking stakeholders.
Qualifications
Minimum
Education: Bachelor's / Master's degree in Computer Science, Computer Engineering, Electrical Engineering, or a related technical field plus 12+ years / 10+ years related industry experience.
OS Expertise: Deep familiarity and hands-on experience with Linux operating systems, including system-level performance tuning and troubleshooting.
Programming Skills: Strong proficiency in programming and scripting languages, specifically Python and C++.
AI/ML Knowledge: Familiarity with modern machine learning frameworks, particularly PyTorch, and a solid understanding of how AI models consume compute and network resources.
Networking & Fabric: Proven experience in performance testing and validating Ethernet switch systems.
Analytical Capabilities: Extensive experience with performance metrics, profiling, and benchmarking tools. Strong problem-solving skills with a proven ability to diagnose root causes in complex, distributed systems.
Preferred
Experience with RDMA (Remote Direct Memory Access) and RoCEv2 (RDMA over Converged Ethernet).
Prior experience building CI/CD pipelines for automated hardware or software performance regression testing.
Familiarity with containerization and orchestration tools (Docker, Kubernetes) used in AI deployments.