We are now looking for a 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:
MS in Computer Science, Electrical Engineering or Computer Engineering (or equivalent 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 modeling.
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 we build on top of them. We integrate and optimize every deep learning framework. We work with the major systems companies and every major cloud service provider to make GPUs available in data centers and in the cloud. We craft computers and software to bring AI to edge devices, such as self-driving cars and autonomous robots. AI has the potential to spur a wave of social progress unmatched since the industrial revolution.
You will also be eligible for equity and benefits.
This posting is for an existing vacancy.
NVIDIA uses AI tools in its recruiting processes.
NVIDIA is committed to fostering a diverse work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.NVIDIA Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about NVIDIA and has not been reviewed or approved by NVIDIA.
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Equity Value & Accessibility — Equity awards and a discounted ESPP are highlighted as core parts of total compensation, enabling employees to share in the company’s success. Stock-based compensation and the two-year lookback ESPP are consistently described as especially valuable.
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Healthcare Strength — Health coverage is portrayed as robust, with comprehensive medical, dental, and vision options alongside mental health support and on-site care resources. Employer HSA contributions and wellness perks reinforce the depth of the offering.
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Retirement Support — Retirement programs are depicted as strong, featuring a meaningful 401(k) match with Roth options and support for Mega Backdoor Roth contributions. These elements position long-term savings as a notable advantage of the total rewards package.
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What We Do
NVIDIA’s invention of the GPU in 1999 sparked the growth of the PC gaming market, redefined modern computer graphics, and revolutionized parallel computing. More recently, GPU deep learning ignited modern AI — the next era of computing — with the GPU acting as the brain of computers, robots, and self-driving cars that can perceive and understand the world. Today, NVIDIA is increasingly known as “the AI computing company.”
