Senior Software Engineer - GPU Local AI Platforms

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 has been transforming computer graphics, PC gaming, and accelerated computing for more than 25 years. It’s a unique legacy of innovation that’s fueled by great technology—and amazing people. Today, we’re tapping into the unlimited potential of AI to define the next era of computing. An era in which our GPU acts as the brains of computers, robots, and self-driving cars that can understand the world. Doing what’s never been done before takes vision, innovation, and the world’s best talent. As an NVIDIAN, you’ll be immersed in a diverse, supportive environment where everyone is inspired to do their best work. Come join the team and see how you can make a lasting impact on the world.
NVIDIA\'s Local AI team is building the software stack that makes large language models and generative AI applications run at maximum efficiency on NVIDIA edge AI hardware. The AI ecosystem moves fast; our job is to make sure end users get the best experience. We own the platform — performance, CI/CD pipelines, validated recipes, and model bring-up infrastructure — that lets developers run groundbreaking LLMs out of the box. The open-source community builds fast; our platform is what turns community innovation into something developers and partners can rely on at scale.

What You\'ll Be Doing

• 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 (attention variants, MoE routing, speculative decoding, multi-token prediction, quantized inference) 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 (memory bandwidth, FLOP/s, interconnect throughput) 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

What We Need To See

• 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

Widely considered to be one of the technology world’s most desirable employers, NVIDIA offers highly competitive salaries and a comprehensive benefits package. As you plan your future, see what we can offer to you and your family www.nvidiabenefits.com/

Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 224,000 USD - 356,500 USD for Level 5, and 272,000 USD - 431,250 USD for Level 6.

You will also be eligible for equity and benefits.

Applications for this job will be accepted at least until July 28, 2026.

This posting is for an existing vacancy.

NVIDIA uses AI tools in its recruiting processes.

NVIDIA is committed to fostering an inclusive work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.