Senior Inference Performance Architect - Deep Learning

Details

We are now looking for a Deep Learning Performance Analysis Architect! NVIDIA is seeking outstanding Performance Analysis Architects to help analyze and accelerate AI application performance at the intersection of both hardware and software. Intelligent machines powered by Artificial Intelligence that can learn, reason and interact with people are no longer science fiction. GPU Deep Learning has provided the foundation for machines to learn, perceive, reason and solve the world's most challenging problems. NVIDIA's GPUs excel at running AI algorithms, and act as the brains of computers, robots and self-driving cars that can perceive and understand the world.

Responsibilities

• Analyze performance and power efficiency of the most important deep learning inference workloads
• Understand and analyze the interplay of hardware and software architectures on forward-looking algorithms, programming models and applications
• Identify and prototype opportunities for performance optimization
• 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 work/research experience
• Solid foundation in machine learning and deep learning
• Excellent programming skills in Python, C, C++
• Strong background in computer architecture
• Experience with performance modeling, architecture simulation, profiling, and analysis
• A track record of creative solutions to technical challenges

Ways to stand out from the crowd:

• CUDA programming skills
• Background with deep neural network training, inference and optimization in leading frameworks (e.g. Pytorch, Tensorflow, TensorRT)
• Experience with the architecture of or workload analysis on GPUs or other DL accelerators

Increasingly known as “the AI computing company”, NVIDIA wants you! Come join our Deep Learning Architecture team, where you can help build real-time, cost-effective computing platforms driving our success in this exciting and rapidly growing field.