NVIDIA

Senior High-Performance LLM Training Engineer

In short

This role optimizes NVIDIA's high-performance LLM training software stack (PyTorch, JAX) across thousands of GPUs and helps shape future GPU hardware roadmaps. It involves profiling and optimizing AI training workloads, building production-quality code across the deep learning stack, supporting MLPerf Training benchmark submissions, and building workload analysis/optimization tools. NVIDIA, a major GPU and AI computing company, requires a PhD + 5 years or MS + 8 years of relevant experience, deep computer/GPU architecture knowledge, performance tuning experience, and strong C++, Python, and CUDA skills. The position is hybrid, based in Santa Clara, CA.

Senior High-Performance LLM Training Engineer
NVIDIA is seeking experienced engineers specializing in performance analysis and optimization to improve the efficiency of LLM training workloads, which are shaping the world's most advanced computing systems. This position focuses on optimizing NVIDIA's high-performance LLM software stack in frameworks like PyTorch and JAX for high-performance training on thousands of GPUs, while also helping shape hardware roadmaps for the next generation of GPUs powering the AI revolution.
What you will be doing:
•Understand, analyze, profile, and optimize AI training workloads on innovative hardware and software platforms.
•Understand the big picture of training performance on GPUs, prioritizing and then solving problems across all state-of-the-art neural networks.
•Implement production-quality software in multiple layers of NVIDIA's deep learning platform stack, from drivers to DL frameworks.
•Build and support NVIDIA submissions to the MLPerf Training benchmark suite.
•Implement key DL training workloads in NVIDIA's proprietary processor and system simulators to enable future architecture studies.
•Build tools to automate workload analysis, workload optimization, and other critical workflows.
What we want to see:
•PhD in Computer Science, Electrical Engineering or Computer Engineering and 5+ years; or MS (or equivalent experience) and 8+ years of meaningful work experience.
•Strong background in deep learning and neural networks, in particular training.
•A deep background in computer architecture and familiarity with the fundamentals of GPU architecture.
•Proven experience analyzing and tuning application performance & processor and system-level performance modelling.
•Programming skills in C++, Python, and CUDA.
GPU computing is the most productive and pervasive platform for deep learning and AI. It begins with the most advanced GPUs and the systems and software built on top of them. NVIDIA integrates and optimizes every deep learning framework, working with major systems companies and every major cloud service provider to make GPUs available in data centers and in the cloud, and crafting computers and software to bring AI to edge devices, such as self-driving cars and autonomous robots.
This opportunity offers the ability to collaborate with some of the most forward-thinking and hard-working people in the world, shaping the future of AI in a creative and autonomous work environment that encourages innovation, working across the full hardware & software stack—from GPU architecture to application code—to achieve optimal performance.
Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 184,000 USD - 287,500 USD for Level 4, and 224,000 USD - 356,500 USD for Level 5. You will also be eligible for equity and benefits.
Applications for this job will be accepted at least until November 1, 2026. This posting is for an existing vacancy. NVIDIA uses AI tools in its recruiting processes.

Stop applying.
Get referred.

$10 in free credits

In just 60 seconds, fully on autopilot — Eraldt's AI agents read your CV, map the roles you fit, find live vacancies and the people who can refer you, write personal emails, and get you referred.

This isn't an application: your CV doesn't go to the company, and we don't promise a referral for this exact role.

Discover who else lands interviews on autopilot