About the job
NVIDIA is building advanced compiler technologies to accelerate AI workloads, and we are looking for an engineer focused on performance validation, analysis, and tracking. In this role, you will work at the intersection of deep learning compilers, GPU systems, and automation infrastructure, ensuring that performance improvements are measurable, scalable, and continuously validated over time.
Responsibilities
Design and develop performance testing frameworks for deep learning compilers and workloads
Build and maintain automated pipelines (CI/CD) to continuously track performance across models, hardware, and compiler changes
Implement benchmarking systems to measure latency, throughput, and efficiency of AI and HPC workloads
Analyze performance trends over time and identify regressions, bottlenecks, and optimization opportunities
Partner with compiler and architecture teams to debug and resolve performance issues
Develop tools and dashboards for performance visualization, reporting, and insights
Enable scalable testing across diverse GPU systems and environments
Improve infrastructure to ensure reliable, reproducible, and high-signal performance data
Qualifications
Minimum
BS, MS, or PhD (or equivalent experience) in Computer Science, Computer Engineering, Electrical Engineering, Mathematics, or related field
5+ years of software engineering experience, including experience in performance engineering, benchmarking, or systems optimization
Strong programming skills in Python (C++ is a plus)
Experience with CI/CD systems and automation frameworks
Familiarity with hardware-aware performance analysis (GPUs, accelerators, or similar systems)
Experience working with deep learning frameworks such as PyTorch, TensorFlow, JAX, or TensorRT
Background in data analysis, profiling, and regression tracking
Ability to debug complex system-level issues across software and hardware layers
Preferred
Experience with GPU performance analysis and optimization
Understanding of compiler internals (LLVM, MLIR, CUDA compilation flow)
Experience building performance dashboards and large-scale telemetry systems
Familiarity with hardware/software co-design or low-level performance tuning
Experience with distributed testing infrastructure or large-scale benchmarking systems