Used Tools & Technologies
Not specified
Required Skills & Competences
Tag name is followed by "@" symbol and proficiency level value.
About proficiency levels:
- 1-2 — basic awareness. Minimal hands-on experience, and a rudimentary understanding of the technology's purpose;
- 3-6 — daily use. Comfortable and regular usage, capable of handling common tasks and challenges related to the technology;
- 7-9 — you are an expert, you can teach others, you know all the pitfalls and tricks;
- 10 — exceptional knowledge, comprehensive understanding, and adeptness in all aspects of the technology, including advanced problem-solving. Think twice before claiming or demanding such level.
Python @ 7
CI/CD @ 4
TensorFlow @ 4
Mathematics @ 4
Data Analysis @ 4
Reporting @ 4
PyTorch @ 4
CUDA @ 4
GPU @ 4
Deep Learning @ 4
AI @ 4
Profiling @ 4
TensorRT @ 4
HPC @ 4
Performance Analysis @ 4
LLVM @ 4
JAX @ 4
- 1-2 — basic awareness. Minimal hands-on experience, and a rudimentary understanding of the technology's purpose;
- 3-6 — daily use. Comfortable and regular usage, capable of handling common tasks and challenges related to the technology;
- 7-9 — you are an expert, you can teach others, you know all the pitfalls and tricks;
- 10 — exceptional knowledge, comprehensive understanding, and adeptness in all aspects of the technology, including advanced problem-solving. Think twice before claiming or demanding such level.
Details
NVIDIA is building advanced compiler technologies to accelerate AI workloads. This role focuses on performance validation, analysis, and tracking at the intersection of deep learning compilers, GPU systems, and automation infrastructure. You will collaborate with compiler developers, infrastructure providers, and hardware teams to build systems that track, analyze, and improve performance across AI workloads.
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 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
Ways to Stand Out
- 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
Compensation & Additional Information
- Base salary range: 152,000 USD - 241,500 USD
- Eligible for equity and benefits
- Applications accepted at least until May 10, 2026
- NVIDIA uses AI tools in its recruiting processes and is an equal opportunity employer