Senior Software Architect, AI Networking
NVIDIA is hiring a Senior Software Architect, AI Networking. Level rates it ; you can apply on NVIDIA.
Level's read on this job
- On Level's scale, this role is Builds AI ●●●●: the job is building AI systems.
- Tools and skills the posting cites include GPUs, Spectrum X and system architecture.
- Level classifies it as a senior role.
- Level lists 2,700+ other open roles at NVIDIA.
AI in this role
Senior Software Architect designing scalable infrastructure and optimization for multi-node LLM inference across GPU clusters.
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 excellent 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. Join our team and discover how you can build a lasting impact on the world.
NVIDIA is seeking a sharp, innovative, and hands-on Architect to help shape the future of LLM inference at scale. Join our dynamic E2E Architecture group, where we build ground breaking systems powering the next generation of generative AI workloads. In this role, you will work across software and hardware domains to design and optimize inference infrastructure for large language models running on some of the most sophisticated GPU clusters in the world. You’ll help define how AI models are deployed and scaled in production, driving decisions on everything from memory orchestration and compute scheduling to inter-node communication and system-level optimizations. This is an opportunity to work with top engineers, researchers, and partners across NVIDIA and leave a mark on the way generative AI reaches real-world applications.
What You’ll Be Doing:
Design and evolve scalable architectures for multi-node LLM inference across GPU clusters.
Develop infrastructure to optimize latency, throughput, and cost-efficiency of serving large models in production.
Collaborate with model, systems, compiler, and networking teams to ensure complete, high-performance solutions.
Prototype novel approaches to KV cache handling, tensor/pipeline parallel execution, and dynamic batching.
Evaluate and integrate new software and hardware technologies relevant to Core Spectrum-X technologies, such as load balancing, telemetry, congestion control, vertical application integration.
Work closely with internal teams and external partners to translate high-level architecture into reliable, high-performance systems.
Author design documents, internal specs, and technical blog posts and contribute to open-source efforts when appropriate.
What We Need to See:
Bachelor’s, Master’s, or PhD in Computer Science, Electrical Engineering, or equivalent experience.
8+ years of experience building large-scale distributed systems or performance-critical software.
Deep understanding of deep learning systems, GPU acceleration, and AI model execution flows and/or high performance networking.
Proven software engineering skills in C++ and/or Python, preferably demonstrate strong familiarity with CUDA or similar platforms.
Strong system-level thinking across memory, networking, scheduling, and compute orchestration.
Excellent communication skills and ability to collaborate across diverse technical domains.
Ways to Stand Out from the Crowd:
Experience working on LLM - training or inference pipelines, transformer model optimization, or model-parallel deployments.
Proven success in profiling and optimizing performance bottlenecks across the LLM training or inference stack.
AI Accelerators and distributed communication patterns, congestion control and/or load balancing.
Shown optimization process for complex systems, deployed at scale to make impact.
Proven experience successfully driving complex organizational processes from planning through implementation.
NVIDIA is widely considered one of the most desirable places to work in tech – we are passionate about what we do and are committed to fostering a culture of perfection, innovation, and collaboration. If you’re excited to help define how the world runs AI at scale, this role is for you. Widely considered to be one of the technology world’s most desirable employers, NVIDIA offers highly competitive salaries and a comprehensive benefits package. While planning your future, explore the benefits available to you and your family at www.nvidiabenefits.com/
How we rate this
Senior Software Architect, AI Networking at NVIDIA rates 90 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.
Builds AI. The job is building AI systems.
- ●●●● Builds AI80 to 100
- ●●●○ Works on AI60 to 79
- ●●○○ Uses AI40 to 59
- ●○○○ Little AI0 to 39
Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.
Prepare for this job
Built from this posting and from similar jobs on Level.
Skills and AI tools this role asks for
Questions you could be asked
- Tell me about a project where system architecture was part of your work. What did you do?
- Tell me about a project where llm inference was part of your work. What did you do?
- Tell me about a project where distributed systems was part of your work. What did you do?
- Tell me about a project where performance optimization was part of your work. What did you do?
- Walk me through how you've used GPUs in your day-to-day work.
Adapt your resume
- List these exact terms on your resume: System Architecture, LLM Inference, Distributed Systems, Performance Optimization, and GPUs. An applicant tracking system matches the wording, not the idea.
- Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
- Lead with what you built, trained or shipped. This role is judged on the AI system itself, not the tools around it.
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