High-Performance LLM Training Engineer - New College Grad 2026

Nvidia

Quick summary

Work type
On-site
Location
Santa Clara, CA
Salary
$124,000–$195,500 / yr
Posted
3 days ago
Nearby
99+ roles within 25 mi

Market check

Salary context

Below market

How this pay compares to similar roles

Similar $195k
This role $160k
$110k most similar roles pay here $251k

This role pays less than 69% of similar roles. Most pay $152,875–$237,625 — the shaded band above. At the midpoint, this role pays about $160k versus about $195k 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 997 open roles on FindRole.

Listed pay typically runs $168,000–$270,250 across 984 roles with salary data.

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At a glance

TL;DR · High-Performance LLM Training Engineer - New College Grad 2026

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NVIDIA seeks a High-Performance LLM Training Engineer with expertise in performance analysis and optimization to enhance the efficiency of large language model training on thousands of GPUs using frameworks like PyTorch and JAX. This role involves profiling AI workloads, optimizing neural network training across various platforms, implementing software in NVIDIA’s deep learning stack, supporting MLPerf submissions, and developing tools for automated workload analysis. Candidates should have an MS in Computer Science or related field, strong knowledge of deep learning and GPU architecture, experience with performance tuning, and proficiency in C++, Python, and CUDA. The position plays a crucial role in shaping future hardware roadmaps and advancing AI capabilities across data centers and edge devices.

Skills

PyTorch JAX CUDA C++ Python GPU MLPerf DeepLearning ComputerArchitecture PerformanceAnalysis SystemSimulation AutomationTools NVIDIA Drivers CI/CD

What you'll do

  • Analyze and optimize AI training workloads on GPUs for high performance.
  • Solve complex performance issues across various state-of-the-art neural networks.
  • Implement software in multiple layers of NVIDIA's deep learning platform stack.
  • Support NVIDIA submissions to the MLPerf Training benchmark suite.
  • Develop tools for automating workload analysis and optimization workflows.

What we're looking for

  • MS in Computer Science, Electrical Engineering or equivalent experience.
  • Strong background in deep learning and neural network training.
  • Expertise in computer architecture and GPU fundamentals.
  • Experience analyzing and tuning application performance.
  • Proficient in C++, Python, and CUDA programming.

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