Senior Deep Learning Performance Architect

Nvidia

Quick summary

Work type
On-site
Location
Santa Clara, CA · Redmond, WA
Salary
$184,000–$287,500 / yr
Posted
3 days ago

Market check

Salary context

Above market

How this pay compares to similar roles

Similar $221k
This role $236k
$168k most similar roles pay here $300k

This role pays more than 68% of similar roles. Most pay $196,750–$246,150 — the shaded band above. At the midpoint, this role pays about $236k versus about $221k for comparable roles.

Based on 240 similar postings.

Employer

About Nvidia

Nvidia is a leading designer of graphics processing units (GPUs) and system-on-chip units, powering gaming, professional visualization, data centers, and artificial intelligence workloads. Industry: Semiconductors & AI Computing

Nvidia currently has 563 open roles on FindRole.

Listed pay typically runs $168,000–$264,500 across 556 roles with salary data.

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At a glance

TL;DR · Senior Deep Learning Performance Architect

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NVIDIA seeks a Senior Deep Learning Performance Architect to join its cutting-edge Deep Learning Architecture team. This role involves developing advanced architectures to enhance deep learning performance and efficiency, analyzing hardware-software interactions, and evaluating PPA trade-offs using high-level simulators in C++/Python. The ideal candidate will collaborate closely with software, product, and research teams to guide the direction of deep learning hardware and software. Required qualifications include an MS or PhD in a relevant field, 6+ years of experience in GPU or Deep Learning ASIC architecture for distributed training and inference, expertise in performance modeling and analysis, strong programming skills in Python, C, and C++, and familiarity with modern transformer-based architectures and leading frameworks like PyTorch and TensorRT. This position offers the opportunity to contribute to real-time computing platforms that drive NVIDIA’s success in AI computing.

Skills

Python C++ PyTorch JAX TensorRT GPU Deep_Learning Performance_Modeling Architecture_Simulation Profiling Analysis Machine_Learning Transformer_Architectures LLM_Optimization SW_HW_Co_Design AI_Engineering

What you'll do

  • Develop innovative deep learning architectures to enhance performance and efficiency.
  • Analyze performance, cost, and power trade-offs using analytical models and simulators.
  • Evaluate PPA for hardware features and system-level architectural decisions.
  • Collaborate with software teams to guide the direction of deep learning HW/SW.
  • Understand and analyze the impact of hardware-software interplay on future algorithms.

What we're looking for

  • MS or PhD in Computer Science, Engineering with 6+ years of relevant experience
  • Strong background in GPU or Deep Learning ASIC architecture for distributed training and inference
  • Experience with performance modeling, simulation, profiling, and analysis
  • Solid foundation in machine learning, deep learning, and transformer-based architectures
  • Expertise in Python, C, C++ programming languages
  • Familiarity with advanced optimizations and SW/HW co-design in LLM training and inference

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