[Remote] Engineering Manager, Deep Learning Inference

Note: The job is a remote job and is open to candidates in USA. NVIDIA is seeking an exceptional manager to lead a world-class engineering team advancing deep learning inference and AI model deployment on NVIDIA GPUs. The role is responsible for shaping strategy and execution for open-source inference frameworks, optimizing large-scale models and inference pipelines, and fostering technical excellence and innovation.

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

Skills

• 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
• 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

• Equity
• Comprehensive benefits package
• Endless opportunities for career advancement

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