Engineering Manager, Deep Learning Inference

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 SGLang, vLLM, and FlashInfer.

Our work enables developers worldwide to harness NVIDIA accelerators for real-time inference at every scale, from datacenter clusters to edge devices.

Responsibilities

• Lead, mentor, and scale a high-performing engineering team focused on deep learning inference and GPU-accelerated software.
• Guide the strategy, roadmap, and execution of NVIDIA's OSS inference frameworks engineering.
• 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.

Requirements

• 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.

Benefits

• Highly competitive salaries
• Comprehensive benefits package
• Equity eligibility