Deep Learning Performance Architect
NVIDIA
Deep Learning Performance Architect
NVIDIA
Designs the chips and software that power AI
NVIDIA is developing GPU and system architectures that accelerate deep learning, automotive and high-performance computing applications. We are looking for an expert deep learning performance architect to join our deep learning modelling, performance projections, analysis and optimization effort. In this position, you will have the chance to optimize deep learning hardware and software architecture and make a significant impact in a dynamic technology focused company
What you’ll be doing:
Analyze performance of various deep learning algorithms on different architectures
Identify architecture and software performance bottlenecks and propose optimizations
Explore new features and hardware capabilities on deep learning applications
What we need to see:
BSc. MS or PhD in relevant discipline (CS, EE, Math, etc.,)
5+ years of working experience in relevant directions will be a plus
Be familiar with GPU or accelerator-based deep learning platform and software stack
A strong background in computer architecture
Experience on system architecture design and performance optimization
Familiar with machine learning and deep learning frameworks
NVIDIA designs the chips and software that power artificial intelligence and sells complete data center systems to cloud providers, AI labs, and other businesses, along with GeForce graphics cards for gamers. Its GPUs, chips that perform thousands of calculations at once, became the main hardware for AI after researchers used them to train the AlexNet image-recognition model in 2012. NVIDIA connects many GPUs with its own processors and networking gear into servers and rack-sized computers, and developers program them through its CUDA software. These systems bring in most of its sales, and it also makes chips for cars and robots. Jensen Huang co-founded NVIDIA in 1993, and it is based in Santa Clara, California.
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