Senior Deep Learning Engineer

Nvidia

Quick summary

Work type
On-site
Location
Redmond, WA
Salary
$152,000–$241,500 / yr
Posted
10 days ago

Market check

Salary context

Below market

How this pay compares to similar roles

Similar $221k
This role $197k
$139k most similar roles pay here $276k

This role pays less than 68% of similar roles. Most pay $195,000–$246,150 — the shaded band above. At the midpoint, this role pays about $197k versus about $221k for comparable roles.

Based on 239 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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View all roles at Nvidia

At a glance

TL;DR · Senior Deep Learning Engineer

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As a Senior Deep Learning Engineer at NVIDIA, you will join a pioneering team focused on advancing the capabilities of artificial intelligence by developing next-generation inference optimizations for frontier workloads including multi-agent AI systems and generative multimodal models. Your daily tasks will involve characterizing these emerging workloads and creating novel methods to optimize them across various tiers of the inference stack, from algorithmic to system level, ensuring efficient deployment in NVIDIA’s growing datacenter infrastructure. You will collaborate closely with diverse teams within NVIDIA, including deep learning research, framework development, and silicon architecture groups, requiring a strong foundation in deep learning, particularly generative models and inferencing, along with experience in modern frameworks like PyTorch and expertise in computer architecture.

Skills

PyTorch Python DeepLearning GenerativeModels InferenceOptimization MultiAgentSystems MultimodalModels CI/CD Git NVIDIAHardware ComputerArchitecture CloudServices Docker Kubernetes

What you'll do

  • Analyze and prototype emerging workloads in multi-agent AI systems and generative multimodal models.
  • Develop novel optimizations for inference-time compute scaling across the entire inference stack.
  • Keep abreast of latest advancements in generative AI research and integrate them into software frameworks.
  • Collaborate with production teams to incorporate cutting-edge advancements into modern deep learning frameworks.
  • Enhance inferencing quality and speed on NVIDIA systems through innovative algorithmic improvements.

What we're looking for

  • Master's degree in Computer Science, Artificial Intelligence, Applied Mathematics, or related fields.
  • At least 5 years of experience in modern deep learning frameworks like PyTorch.
  • Strong foundation in deep learning with emphasis on generative models and inferencing.
  • Published research or significant contributions to deep learning, especially in inference-time compute and multimodal generation.
  • Experience prototyping or deploying agentic AI systems and multimodal generation models.
  • Familiarity with computer architecture and its relation to AI algorithms development.

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