NVIDIA
Full TimeYou'll benchmark and optimize deep learning workloads on GPU and NPU architectures, building performance models and projections for LLMs and generative AI. The role requires a bachelor's degree minimum in computer science, electrical engineering, or related field, plus three or more years in relevant performance optimization work. You need strong knowledge of GPU platforms, computer architecture, deep learning frameworks, and LLM kernel optimizations, along with experience in system design and AI agents for engineering workflows.
Written from this posting by Neural Jobs AI. The full description is below.
NVIDIA is developing processor and system architectures that accelerate deep learning on edge devices, workstations, and data center GPUs for a variety of applications including automotive, robotics, large language models and AI generative models. We are looking for an expert deep learning system performance architect to join our deep learning modelling, performance optimization, projections, and analysis effort. In this position, you will have the chance to optimize deep learning hardware and software architecture and make the significant impact in a dynamic technology focused company
What you’ll be doing:
Benchmark and analyze performance of various machine learning/deep learning workloads across GPU- and NPU-based architectures
Build and validate performance models, and deliver performance projections and insights for deep learning (LLM/GenAI) workloads on emerging architectures
Identify architecture, software and system performance bottlenecks and propose actionable optimizations
Explore and evaluate new software/hardware capabilities and translate them into measureable application gains
Leverage AI agents to accelerate performance investigation and engineering workflows
What we need to see:
BSc. MS or PhD in relevant discipline (CS, EE, Math, etc.,)
3+ years of working experience in relevant directions will be a plus
Familiar with GPU or Accelerator-based deep learning platform and software stack
A strong background in computer architecture
Familiar with LLM or generative AI deep learning algorithms and kernel optimizations
Experience in system architecture design and performance optimization
Familiar with machine learning and deep learning frameworks
Hands-on experience using AI agents to assist daily engineering work
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NVIDIA builds the GPUs and the CUDA software stack that most modern AI is trained and served on, along with its own research in graphics, robotics and foundation models.
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