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
CUDA @ 6
Debugging @ 4
Deep Learning
GPU @ 6
LLM
Machine Learning @ 4
Networking @ 4
Python @ 4
SGLang @ 4
Security @ 4
Slurm @ 4
System Architecture
TensorRT @ 4
vLLM @ 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 seeking a Senior Validation Engineer for the DGX Server Product Engineering Team. You will work with hardware and software engineers to develop and implement complex automated test plans for GPU-accelerated computing products.
Responsibilities
- Perform system architecture, design, performance modeling, and estimation across new models and packages.
- Enable GPU SKU bring-up, validation, and model enablement.
- Develop system-level stress and performance testing strategies using Deep Learning and AI applications.
Requirements
- Ability to work onsite in a hardware lab environment five days per week.
- Bachelor of Science in Electrical Engineering, Computer Engineering, or equivalent experience.
- At least five years of experience validating and debugging complex systems.
- Experience developing and running real-world machine learning and large language model workloads.
- Mandatory experience with Dynamo, TensorRT, Slurm, and BCM.
- Preferred knowledge of vLLM and SGLang.
- Proficiency in CUDA, cuBLAS, and CUTLASS.
- Deep understanding of computing architectures.
- Python programming experience, including running simulators.
- Experience with data center products, including system management, security, networking, and storage.
Preferred Qualifications
- Background with x86 and Arm server architectures and accelerated GPU computing.
- Track record of continuous process improvement and a passion for tools and automation.
Benefits
NVIDIA offers competitive salaries, equity, and a comprehensive benefits package. The company is committed to fostering a diverse work environment and is an equal opportunity employer. Applications will be accepted at least until April 5, 2026.
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