NVIDIA is developing processor and system architectures that accelerate deep learning and high-performance computing applications. We are looking for a talented deep learning performance architect to join our AI performance modelling, analysis and optimization efforts. In this position, you will have a chance to work on DL performance analysis, and optimization on state-of-the-art hardware architectures for various LLM and Multi-model workloads. You will make your contributions to our dynamic technology focused company.
What you'll be doing:
- Analyze state-of-the-art DL networks (LLM, VLA, and Multimodal model etc.), identify and prototype performance opportunities to influence SW and Architecture team for NVIDIA's current and next gen inference products.
- Develop analytical models for the state-of-the-art deep learning networks and algorithm to innovate processor and system architectures design for performance and efficiency.
- Specify hardware/software configurations and metrics to analyze performance, power, and accuracy in existing and future uniprocessor and multiprocessor configurations.
- Collaborate across the company to guide the direction of next-gen deep learning HW/SW by working with architecture, software, and product teams.
What we need to see:
- Pursuing BS or higher degree in a relevant technical field (CS, EE, CE, Math, etc.).
- Strong programming skills in Python, C, C++.
- Strong background in computer architecture.
Ways to stand out from the crowd:
- Experience with GPU Computing and parallel programming models such as CUDA and OpenCL.
- Experience with workload analysis on other deep learning accelerators.
- Background with deep neural network training, inference and optimization in leading frameworks (e.g. Pytorch, Tensorflow, TensorRT).
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