Engineering Manager, Deep Learning Inference

About the role

NVIDIA is seeking an exceptional Manager, Deep Learning Inference Software, to lead a world-class engineering team advancing the state of AI model deployment. You will shape the software powering today’s most sophisticated AI systems — from large language models to multimodal generative AI — all accelerated on NVIDIA GPUs. The Deep Learning Inference team develops and optimizes open-source frameworks that make AI deployment scalable, efficient, and accessible — including vLLM / SGLang, and FlashInfer. Our work enables developers worldwide to harness NVIDIA accelerators for real-time inference at every scale, from datacenter clusters to edge devices.

What you'll be doing:

- Lead, mentor, and scale a high-performing engineering team focused on deep learning inference and GPU-accelerated software.

- Drive the strategy, roadmap, and execution of NVIDIA’s inference frameworks engineering, focusing on Client AI.

- Partner with internal compiler, libraries, and research teams to deliver end-to-end optimized inference pipelines across NVIDIA accelerators.

- Oversee performance tuning, profiling, and optimization of large-scale models for LLM, multimodal, and generative AI applications.

- Guide engineers in adopting best practices for CUDA, Triton, CUTLASS, and multi-GPU communications (NIXL, NCCL, NVSHMEM).

- Represent the team in roadmap and planning discussions, ensuring alignment with NVIDIA’s broader AI and software strategies.

- Foster a culture of technical excellence, open collaboration, and continuous innovation.

What we need to see:

- MS, PhD, or equivalent experience in Computer Science, Electrical/Computer Engineering, or a related field.

- 6+ overall years of software development experience, including 3+ years in technical leadership or engineering management.

- Strong background in C/C++ software design and development; proficiency in Python is a plus.

- Hands-on experience with GPU programming (CUDA, Triton, CUTLASS) and performance optimization.

- Proven record of deploying or optimizing deep learning models in production environments.

- Experience leading teams using Agile or collaborative software development practices.

Ways to Stand out from The Crowd:

- Significant open-source contributions to deep learning or inference frameworks such as PyTorch, vLLM / SGLang, Triton, or TensorRT-LLM.

- Deep understanding of multi-GPU communications (NIXL, NCCL, NVSHMEM) and distributed inference architectures.

- Expertise in performance modeling, profiling, and system-level optimization across CPU and GPU platforms.

- Proven ability to mentor engineers, guide architectural decisions, and deliver complex projects with measurable impact.

- Publications, patents, or talks on LLM serving, model optimization, or GPU performance engineering.

With highly competitive salaries and a comprehensive benefits package, NVIDIA is widely considered to be one of the technology world's most desirable employers. We have some of the most forward-thinking and hardworking people in the world working for us and, our rapid growth means endless opportunities for career advancement. If you’re a passionate technical leader ready to shape the future of AI inference frameworks — and build the software that powers the world’s most advanced models — we’d love to hear from you.