Senior Systems Performance Engineer

Responsibilities: Develop and tune synthetic GPU workloads with CUDA and AI frameworks to stress hardware subsystems and broaden validation coverage Onboard and adapt AI training/inference workloads to new GPU platforms Apply architectural analysis and create system-level performance models to characterize workload behavior and identify bottlenecks Design controlled experiments, compare model predictions with telemetry and results, and refine workloads and test strategies Analyze performance anomalies and failures; create reproducible cases and coordinate with architecture, design, and software teams to drive fixes Build automation for workload deployment, experiment execution, telemetry collection, and analysis across systems and configurations

Key requirements: BS in Computer Engineering, Electrical Engineering, Computer Science, or related field, or equivalent experience 5+ years in systems performance engineering, performance modeling, accelerated computing, or complex system development/debug Deep understanding of computer architecture, including GPU/CPU execution, memory hierarchies, interconnects and their impact on performance Experience developing analytical or simulation-based performance models and validating against measurements Hands-on experience deploying, profiling, and tuning AI workloads using PyTorch, TensorRT-LLM, vLLM, or SGLang Strong Python programming and data analysis skills for experimental design and telemetry interpretation Working knowledge of CUDA and GPU profiling tools; ability to map application behavior to hardware utilization Experience debugging workloads and system behavior in Linux and collaborating across hardware/software teams collaborative problem-solving strong communication CUDA GPU profiling tools PyTorch