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Principal High-Performance LLM Training Engineer

NVIDIA· Engineer, Sys SW
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Santa Clara, CA$272k - $431.3kfull-timePosted 5mo ago

About the role

Lead performance optimization for large-scale AI training and post-training workloads across NVIDIA’s hardware and software stack, focusing on frontier-scale LLM workloads and providing technical leadership across frameworks, GPU architecture, and systems software.

Responsibilities

  • Lead end-to-end performance analysis and optimization of innovative LLM pre-training and post-training workloads on the latest NVIDIA hardware and software platforms.
  • Drive workloads closer to speed-of-light performance by identifying and removing bottlenecks across compute, memory, communication, scheduling, parallelism strategy, kernel efficiency, framework overhead, and system-level scaling.
  • Develop production-quality software, tools, models, benchmarks, and analysis infrastructure that improve training performance, efficiency, and developer velocity across NVIDIA’s AI software stack.
  • Build and refine performance models, workload characterizations, and simulation methodologies to guide future GPU, networking, system, and software architecture decisions.
  • Serve as a technical authority for AI training performance, partnering closely with teams across GPU architecture, systems, CUDA libraries, compilers, networking, frameworks, product management, and applied AI.
  • Translate workload insights into concrete hardware and software recommendations, and advocate for changes that improve performance and efficiency across the AI ecosystem.
  • Mentor and provide technical leadership to engineers across the organization, helping establish best practices for large-scale AI performance analysis and optimization.

Qualifications

  • A MS, or PhD (or equivalent experience) in Computer Science, Electrical Engineering, Computer Engineering, or a related field, with 12+ years of relevant work or research experience.
  • Demonstrated principal-level technical impact in large-scale AI training systems, GPU performance optimization, distributed systems, high-performance computing, ML frameworks, compilers/runtimes, or hardware/software co-design.
  • Deep hands-on experience analyzing and optimizing performance of large-scale deep learning workloads, especially transformer-based models, LLM pre-training, reinforcement learning, fine-tuning, or other post-training workloads.
  • Strong understanding of GPU and AI accelerator architecture from individual accelerators to datacenter-scale systems.
  • Experience with distributed training techniques such as data parallelism, tensor parallelism, pipeline parallelism, expert parallelism, sequence parallelism, activation checkpointing, mixed precision training, and communication/computation overlap.
  • A strong track record of using profiling, tracing, benchmarking, and performance modeling tools to diagnose complex bottlenecks and drive measurable improvements.
  • Excellent communication and technical leadership skills, with the ability to influence architecture and software decisions across multiple teams without relying on direct authority.

Skills

Required:Large-scale AI training performance optimizationProfiling, tracing, benchmarking, and performance modelingDistributed training techniquesCross-functional technical leadership

Benefits

  • equity
  • benefits (details at NVIDIA benefits page)
Last updated Jul 3, 2026. Interested? Apply on the company site.

About NVIDIA

World Leader in AI Computing

NVIDIA invents the GPU and drives advances in AI, HPC, gaming, creative design, autonomous vehicles, and robotics.

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