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NVIDIA

Designs the chips and software that power AI

Senior Software Architect - AI Networking

Applied Machine LearningConfirmed live in the last 24 hours
Full-timeNo salary listedIn-person

About the Job

Requirements

  • A bachelor's, master's, or PhD in Computer Science, Electrical Engineering, or equivalent experience.
  • At least 8 years of experience building large-scale distributed systems or performance-critical software.
  • Deep understanding of deep learning systems, GPU acceleration, AI model execution flows, and/or high-performance networking.
  • Proven software engineering skills in C++ and/or Python, preferably with strong familiarity with CUDA or similar platforms.
  • Strong system-level thinking across memory, networking, scheduling, and compute orchestration.
  • Ability to collaborate across diverse technical domains.

Responsibilities

  • Design and evolve scalable architectures for multi-node large language model 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 approaches to key-value cache handling, tensor/pipeline parallel execution, and dynamic batching.
  • Evaluate and integrate software and hardware technologies relevant to Core Spectrum-X technologies, including load balancing, telemetry, congestion control, and vertical application integration.
  • Work with internal teams and external partners to translate high-level architecture into reliable, high-performance systems.
  • Author design documents, internal specifications, and technical blog posts, and contribute to open-source efforts when appropriate.

Desired Qualifications

  • Experience working on large language model training or inference pipelines, transformer model optimization, or model-parallel deployments.
  • Proven success profiling and optimizing performance bottlenecks across the large language model training or inference stack.
  • Experience with AI accelerators and distributed communication patterns, congestion control, and/or load balancing.
  • Demonstrated optimization of complex systems deployed at scale to make an impact.
  • Proven experience driving complex organizational processes from planning through implementation.

About the Company

NVIDIA

Designs the chips and software that power AI

NVIDIA designs the chips and software that power artificial intelligence and sells complete data center systems to cloud providers, AI labs, and other businesses, along with GeForce graphics cards for gamers. Its GPUs, chips that perform thousands of calculations at once, became the main hardware for AI after researchers used them to train the AlexNet image-recognition model in 2012. NVIDIA connects many GPUs with its own processors and networking gear into servers and rack-sized computers, and developers program them through its CUDA software. These systems bring in most of its sales, and it also makes chips for cars and robots. Jensen Huang co-founded NVIDIA in 1993, and it is based in Santa Clara, California.

Company size
10,001+
Company stage
IPO
Headquarters
Santa Clara, CA
Founded
1993

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