Senior Deep Learning Performance Architect

NVIDIA•Redmond, CA

11h

About The Position

We are now seeking a Senior Deep Learning Performance Architect! NVIDIA is looking for outstanding Performance Architects with a background in performance analysis, performance modeling, and AI/deep learning to help analyze and develop the next generation of architectures that accelerate AI and high-performance computing applications. What you’ll be doing: Develop innovative architectures to extend the state of the art in deep learning performance and efficiency Analyze performance, cost and power trade-offs by developing analytical models, simulators and test suites Understand and analyze the interplay of hardware and software architectures on future algorithms, programming models and applications Evaluate PPA (performance, power, area) for hardware features and system level architectural trade-offs. Develop high level simulators in C++/Python Actively collaborate with software, product and research teams to guide the direction of deep learning HW and SW

Requirements

MS or PhD in Computer Science, Computer Engineering, Electrical Engineering or equivalent experience
6+ years of relevant meaningful work experience
Strong background in GPU or Deep Learning ASIC architecture for distributed training and/or inference spanning multi-chip/multi-node
Experience with performance modeling, architecture simulation, profiling, and analysis
Solid foundation in machine learning and deep learning. Understanding of modern transformer-based architectures and their performance at scale.
Strong programming skills in Python, C, C++

Nice To Haves

Background with deep neural network training, inference and optimization in leading frameworks (e.g. Pytorch, JAX, TensorRT)
Familiarity with advanced optimizations and SW/HW co-design in LLM training and inference
Exposure to using AI to accelerate SW engineering
Demonstration of self-motivation and creative / critical thinking

Responsibilities

Develop innovative architectures to extend the state of the art in deep learning performance and efficiency
Analyze performance, cost and power trade-offs by developing analytical models, simulators and test suites
Understand and analyze the interplay of hardware and software architectures on future algorithms, programming models and applications
Evaluate PPA (performance, power, area) for hardware features and system level architectural trade-offs.
Develop high level simulators in C++/Python
Actively collaborate with software, product and research teams to guide the direction of deep learning HW and SW