Deep Learning Performance Architect
At a glance
Mid-level AI Research role at NVIDIA. China, Shanghai · full-time.
Pay not stated AI Research salary
aimlroles summary, based on the employer's posting.
What you'll do
- Measure deep learning workloads across different hardware and software architectures
- Find performance limits in both the architecture and its software
- Assess new features and system setups for speed and energy-efficiency gains
What you bring
- A relevant degree in computer science, electrical engineering, mathematics or a related discipline
- A solid grasp of computer architecture and experience designing systems and improving their performance
- Familiarity with GPU- or accelerator-based deep learning platforms, software stacks and machine learning frameworks
Who this fits
This role suits you if you bring hands-on experience analyzing deep learning performance and optimizing system architectures. The posting describes relevant work experience as a plus, including hardware systems, data-center hardware or large language model workloads.
From the employer
NVIDIA is developing GPU and system architectures that accelerate deep learning 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 analyze deep learning performance on different 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 workloads on different architectures
Identify architecture and software performance bottlenecks
Explore new features and system configurations to achieve better performance and energy efficiency
What we need to see:
BSc. MS or PhD in relevant discipline (CS, EE, Math, etc.,)
3+ years of working experience in relevant directions (e.g., hardware system, datacenter hardware, LLM workloads on data center) 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