Senior Deep Learning Tools Engineer – CUDA Tile

About the role

NVIDIA is building advanced compiler technologies to accelerate AI workloads, and we are looking for an engineer focused on performance validation, analysis, and tracking.

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

Requirements

• 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

Qualifications

• 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

Skills

• 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

Benefits

• Competitive salaries
• Comprehensive benefits package
• Equity

Pay

• Base salary range: 152,000 USD - 241,500 USD

Schedule

• Full-time