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NVIDIA

Designs the chips and software that power AI

Distinguished Engineer - Scaled Out Inferencing

Applied Machine LearningConfirmed live in the last 24 hours
Full-timeRemote

About the Job

Requirements

  • At least 16 years in technical roles, with a recent long-term focus on AI infrastructure and more recent direct experience in large-scale inference orchestration.
  • At least 7 years of leadership experience.
  • A BS/MS or higher, or equivalent experience, in systems/software engineering or related engineering fields.
  • A proven track record building secure, highly available, and durable production distributed systems.
  • Proficiency in GPU architecture, hardware acceleration, low-level performance tuning, CUDA, kernels, and cloud-native architectures for multi-tenant model serving.
  • A record of delivering technically complex solutions that provide transparency into resource utilization, performance, and operational insights.
  • Ability to develop consensus and organizational alignment across technical leadership and senior corporate leadership, synthesize cross-functional needs into architecture and design, and guide execution across diverse teams.
  • Ability to collaborate and influence in engineering engagements and communicate with peers, partners, and high-performance and accelerated-computing customers.

Responsibilities

  • Architect distributed pipelines and define and drive technical implementation of high-throughput, low-latency distributed inference systems for massive-scale AI workloads.
  • Drive hardware-software co-optimization and performance tuning at the kernel and driver level, optimizing GPU resource management and hardware acceleration for production-grade model serving.
  • Guide and influence open-source projects including Dynamo and TensorRT-LLM, and ecosystem projects including vLLM, SGLang, Linux, Kubernetes, and Ray, to advance inferencing on NVIDIA accelerated hardware.
  • Orchestrate model lifecycles and lead the strategy for model management, including automated deployment, versioning, and intelligent scaling across cloud and datacenter environments.
  • Collaborate with customers, infrastructure providers, and partners to ensure NVIDIA solutions meet performance and availability needs.
  • Lead technical planning and continuous evolution across the full software and systems lifecycle, from ideation, architecture, design, development, and deployment through operations and lifecycle management.
  • Develop NVIDIA's global strategy for scaled-out AI inferencing and define the technical roadmap for deployment, versioning, and automated scaling across enterprise and cloud environments.

Desired Qualifications

  • Real-world experience building systems to support AI/ML workloads.
  • Direct experience designing, developing, delivering, and operating secure, highly available, scaled-out systems in enterprise and cloud environments.
  • A demonstrated history of creating scalable processes and extensible systems that facilitate cross-functional collaboration and operations at scale.
  • Familiarity with open-source ecosystems and projects such as Dynamo, TensorRT-LLM, vLLM, SGLang, and Ray, and ability to collaborate and influence open-source project governance and technical direction on behalf of NVIDIA, customers, and partners.

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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