Senior GPU AI Platforms Engineer - Edge LLM Inference

NVIDIA; Durham (NC); On-site; USD 224,000 - 356,500; Full time

Benefits offered by this job

Equity

Benefits

Job summary

NVIDIA’s Local AI team is building the software stack for running large language models and generative AI applications efficiently on NVIDIA edge AI hardware. This role focuses on performance analysis, model validation, and developing inference recipes across multi-node configurations.

The candidate will work with CUDA/C++, Triton, and Python, evaluating new architectures, implementing optimizations, and collaborating with communities and partners to ensure robust model bring-up on NVIDIA GPUs.

Qualifications

• BS, MS, or PhD in Computer Science, Computer Engineering, Electrical Engineering, or equivalent experience.
• 12+ years of software engineering with depth in GPU computing, ML systems, or high-performance inference
• Strong Python or C++ programming, software design, and software engineering skills.
• Hands-on experience with GPU kernel development or optimization (CUDA/C++, Triton, or equivalent) — you understand how thread blocks, memory hierarchy, and warp execution affect real-world performance
• Working knowledge of LLM inference internals: attention mechanisms, KV-cache management, continuous batching, quantization formats, and tensor parallelism
• Container engineering expertise: multi-architecture Docker or OCI builds, layer optimization, runtime configuration, NVIDIA Container Toolkit
• Strong analytical skills: ability to form a performance hypothesis, design an experiment, interpret results, and communicate findings clearly

Responsibilities

• Track and evaluate innovations in leading open-source LLM inference frameworks — identify performance-critical features and algorithmic improvements relevant to NVIDIA edge AI hardware
• Analyze how new model architectures and inference algorithms map onto NVIDIA GPU architecture — identify mismatch, fallback paths, and optimization opportunities
• Characterize multi-node inference behavior: collective communication primitives (NCCL/RCCL), topology-aware all-reduce strategies, and parallelism efficiency on edge cluster configurations
• Produce performance analysis reports mapping theoretical hardware limits to observed inference throughput, latency, and utilization
• Own the model validation workflow for new model releases: architecture compatibility assessment, inference recipe development, performance characterization, and publication to developer recipe sites
• Develop and maintain developer-facing inference recipes: keep them accurate as frameworks evolve, automate staleness detection, and build feedback loops from CI results to recipe updates
• Engage with community and partners on model bring-up questions; serve as the technical point of contact for hardware-specific inference issues related to partner concerns

Skills

Python

C++

CUDA

Triton

Performance analysis

Container tooling

Education

BS/MS/PhD in CS/CE/EE

Tools

Docker

OCI

Job description

NVIDIA’s Local AI team is building the software stack for running large language models and generative AI applications efficiently on NVIDIA edge AI hardware. This role focuses on performance analysis, model validation, and developing inference recipes across multi-node configurations.

The candidate will work with CUDA/C++, Triton, and Python, evaluating new architectures, implementing optimizations, and collaborating with communities and partners to ensure robust model bring-up on NVIDIA GPUs.