Tech Stack
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.
AI @ 6
Debugging @ 6
Deep Learning
Distributed Systems @ 6
Experimentation
GenAI
Generative AI
LLM
Profiling @ 6
Python @ 6
- 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
Senior System Software Engineer to own and advance the AI-Perf analysis, NVIDIA’s flagship framework for benchmarking, experimentation, and analysis of LLMs, Generative AI, and deep learning inference workloads. In this role, you’ll combine systems research, distributed systems engineering, and applied AI, enabling reproducible performance evaluation, influencing internal platforms, and providing tooling that empowers researchers and engineers globally.
Responsibilities
- Lead the design, development, and roadmap of AI-Perf, defining benchmarking methodologies, performance metrics, and reproducible experimental workflows.
- Build scalable and high-performance features to measure latency, throughput, and efficiency across AI models and distributed systems.
- Partner with AI researchers, platform teams, and engineers to translate experimental challenges into robust, user-friendly performance tooling.
- Integrate AI-Perf with the Dynamo Inference Stack, other NVIDIA inference stacks, and open-source inference frameworks, delivering end-to-end performance insights for researchers and production users.
Requirements
- Bachelor’s, Master’s, or PhD in Computer Science, Computer Engineering, or related field—or equivalent experience.
- 3+ years of experience in systems software, distributed performance engineering, or AI infrastructure research.
- Expert-level Python skills, including profiling, optimization, automation, and debugging of complex systems.
- Deep knowledge of distributed systems concepts, including scalability, concurrency, fault tolerance, and performance trade-offs.
Benefits
- Eligible for equity and benefits.
With highly competitive salaries and a comprehensive benefits package, NVIDIA is widely considered to be one of the technology world's most desirable employers.
Your base salary will be determined based on your location, experience, and the pay of employees in similar positions.
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