NVIDIA
AI
PrincipalHigh-PerformanceLLMTrainingEngineer
Neural analysis suggests this role is
optimal for Principal candidates.
“Principal High-Performance LLM Training Engineer at NVIDIA. Skills: High-Performance LLM Training, AI Training Performance Optimization, Distributed Systems, GPU Architecture, Deep Learning Frameworks. 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,”
What You'll Achieve.
drive improvements across frameworks such as PyTorch, JAX, NeMo, and NeMo RL; help shape future NVIDIA GPU, system, and software roadmaps; directly improving performance directly; setting technical direction; raising the bar for the organization; influencing multi-functional decisions across NVIDIA; improve training performance, efficiency, and developer velocity; guide future GPU, networking, system, and software architecture decisions; advocate for changes that improve performance and efficiency across the AI ecosystem; establish best practices for large-scale AI performance analysis and optimization
Industry & Context.
analyze and optimize frontier-scale LLM workloads; identify and remove bottlenecks; diagnose complex bottlenecks and drive measurable improvements
What They're Looking For.
Must Have
MS, or PhD (or equivalent experience) in Computer Science, Electrical Engineering, Computer Engineering, or a related field, 12+ years of relevant work or research experience, Demonstrated principal-level technical impact in one or more of the following areas: 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, 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 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
What You'll Do.
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
and system-level scaling
Develop production-quality software
and analysis infrastructure that improve training performance
and developer velocity across NVIDIA’s AI software stack
Build and refine performance models
workload characterizations
and simulation methodologies to guide future GPU
and software architecture decisions
Serve as a technical authority for AI training performance
partnering closely with teams across GPU architecture
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
How You'll Work.
Team & Collaboration
partnering closely with teams across GPU architecture, systems, CUDA libraries, compilers, networking, frameworks, product management, and applied AI; influence architecture and software decisions across multiple teams without relying on direct authority
Communication Scope
Excellent communication and technical leadership skills; ability to influence architecture and software decisions across multiple teams without relying on direct authority
Full Job Description
NVIDIA is seeking a Principal Engineer to drive the performance of large-scale AI training and post-training workloads across NVIDIA’s full hardware and software stack. This role sits at the intersection of distributed training, GPU architecture, systems software, deep learning frameworks, and performance engineering. You will analyze and optimize frontier-scale LLM workloads running on thousands of GPUs, drive improvements across frameworks such as PyTorch, JAX, NeMo, and NeMo RL, and use insights from real workloads to help shape future NVIDIA GPU, system, and software roadmaps. We are looking for a deeply technical leader who can operate across abstraction layers: from application-level training behavior to framework/runtime internals, CUDA libraries, communication collectives, memory systems, networking, and GPU architecture. At this level, success means both directly improving performance directly as well as setting technical direction, raising the bar for the organization, and influencing multi-functional decisions across NVIDIA. ****What you will be doing:**** * 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,
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