Principal High-Performance LLM Training Engineer

Optimize frontier-scale LLM workloads across PyTorch, JAX, NeMo, and NeMo RL running on thousands of GPUs.

Originally posted 30 October 2026 by the employer. Relisted by the employer on 31 October 2026.

About the role

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. You will analyze and optimize frontier-scale LLM workloads running on thousands of GPUs, driving improvements across frameworks such as PyTorch, JAX, NeMo, and NeMo RL.

What you'll do

• Lead end-to-end performance analysis and optimization of LLM pre-training and post-training workloads on 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 to 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 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 mentor engineers across the organization.

What you'll need

• 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.
• 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.
• 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.
• Track record of using profiling, tracing, benchmarking, and performance modeling tools to diagnose complex bottlenecks and drive measurable improvements.

Skills: LLM workloads, GPU optimization, PyTorch, JAX, NeMo, CUDA libraries, AI training performance

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