Computer Hardware Manufacturing👥 10,001+ employees📍 Santa Clara, CA
Since its founding in 1993, NVIDIA (NASDAQ: NVDA) has been a pioneer in accelerated computing. The company’s invention of the GPU in 1999 sparked the growth of the PC gaming market, redefined compute…
Job Overview
NVIDIA is looking for an outstanding AI DevOps Engineering Manager to lead and expand our next-gen inference operations infrastructure. Join us in transforming AI inference delivery, supporting NVIDIA's innovative products like Dynamo, Triton, NIXL, and our quickly growing range of AI inference solutions. This role is essential for our GitHub First initiative, enabling public CI/CD infrastructure with GPU and Kubernetes capabilities to deliver high-throughput, low-latency inferencing solutions in distributed environments. Lead a team ensuring our AI products achieve outstanding performance and reliability worldwide.
Key Responsibilities
Supervise a team of DevOps engineers with expertise in AI inference infrastructure, test automation (SDET), and Infrastructure as Code (IaC)
Architect and implement scalable test automation strategies for AI inference workloads, including performance benchmarking and automated quality gates
Lead the maintenance of our GitHub First public CI infrastructure, focusing on single/multi-GPU testing, Kubernetes multi-node GPU testing, and CSP validation
Drive Infrastructure as Code efforts by employing Terraform, Ansible, and Kubernetes to support scaling across multiple clouds and lead GPU clusters effectively
Attain operational proficiency encompassing 24x7 on-call rotations, SRE methodologies, automated monitoring, and self-repairing systems to guarantee uptime exceeding 99.9%
Lead release coordination, cost optimization, and management of multi-cloud deployments
Required Qualifications
Bachelor's/Master's degree in Computer Science, Engineering, or equivalent experience
4+ years leading DevOps/SRE organizations with direct SDET leadership experience
8+ years hands-on experience in software development, test automation, or infrastructure engineering with AI/ML or GPU-intensive workloads
Proficiency in Infrastructure as Code (IaC) platforms: Terraform, Ansible, or CloudFormation with exposure to multiple cloud environments (AWS, GCP, Azure, OCI)
Strong technical leadership in test automation frameworks, CI/CD pipeline development, and quality engineering practices
Familiarity with containerization and orchestration tools such as Docker and Kubernetes for leading AI/ML workloads and GPU resources
Proven success building and scaling teams in fast-paced, high-growth environments
Effective interpersonal skills to collaborate with remote teams and build agreement
Proficiency in Python, Rust, or related programming languages along with the capability to engage in architecture conversations
Demonstrated history of operational proficiency encompassing 24x7 on-call oversight, SRE methodologies, and robust high-availability infrastructures
About NVIDIA
NVIDIA is a leading technology company focused on artificial intelligence and visual computing. The company is known for its innovative products and solutions in the AI and GPU space.
