Engineering Manager - AI DevOps at NVIDIA

Santa Clara, California, United States

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Not SpecifiedCompensation

Senior (5 to 8 years), Expert & Leadership (9+ years)Experience Level

Full TimeJob Type

UnknownVisa

Technology, Artificial IntelligenceIndustries

Requirements

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

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

Skills

Key technologies and capabilities for this role

KubernetesTerraformAnsibleGPUCI/CDIaCSRESDETTest AutomationMulti-cloudAI InferenceMonitoringCloudFormation

Questions & Answers

Common questions about this position

What experience is required to qualify for this Engineering Manager role?

Candidates need a Bachelor's/Master's degree in Computer Science or Engineering, 4+ years leading DevOps/SRE organizations with SDET leadership, and 8+ years hands-on in software development, test automation, or infrastructure engineering with AI/ML or GPU workloads.

What key technical skills are essential for this position?

Proficiency in Infrastructure as Code tools like Terraform, Ansible, or CloudFormation across multiple clouds (AWS, GCP, Azure, OCI), containerization with Docker and Kubernetes for AI/ML workloads, and programming in Python, Rust, or similar is required.

Is remote work possible for this role?

The job mentions effective interpersonal skills to collaborate with remote teams, but no explicit remote or location policy is stated.

What does the role involve in terms of team leadership and operations?

You will supervise a team of DevOps engineers, SDETs, and IaC experts, lead 24x7 on-call rotations, SRE methodologies, automated monitoring, and ensure high availability with uptime exceeding 99.9%.

What makes a candidate stand out for this Engineering Manager position?

Stand out with experience in GitHub Actions CI/CD, releasing open-source AI software, and deep expertise in NVIDIA technologies like CUDA, TensorRT, Dynamo, and Triton Inference Server.

NVIDIA

Designs GPUs and AI computing solutions

About NVIDIA

NVIDIA designs and manufactures graphics processing units (GPUs) and system on a chip units (SoCs) for various markets, including gaming, professional visualization, data centers, and automotive. Their products include GPUs tailored for gaming and professional use, as well as platforms for artificial intelligence (AI) and high-performance computing (HPC) that cater to developers, data scientists, and IT administrators. NVIDIA generates revenue through the sale of hardware, software solutions, and cloud-based services, such as NVIDIA CloudXR and NGC, which enhance experiences in AI, machine learning, and computer vision. What sets NVIDIA apart from competitors is its strong focus on research and development, allowing it to maintain a leadership position in a competitive market. The company's goal is to drive innovation and provide advanced solutions that meet the needs of a diverse clientele, including gamers, researchers, and enterprises.

Santa Clara, CaliforniaHeadquarters

1993Year Founded

$19.5MTotal Funding

IPOCompany Stage

Automotive & Transportation, Enterprise Software, AI & Machine Learning, GamingIndustries

10,001+Employees

Benefits

Company Equity

401(k) Company Match

Risks

Increased competition from AI startups like xAI could challenge NVIDIA's market position.

Serve Robotics' expansion may divert resources from NVIDIA's core GPU and AI businesses.

Integration of VinBrain may pose challenges and distract from NVIDIA's primary operations.

Differentiation

NVIDIA leads in AI and HPC solutions with cutting-edge GPU technology.

The company excels in diverse markets, including gaming, data centers, and autonomous vehicles.

NVIDIA's cloud services, like CloudXR, offer scalable solutions for AI and machine learning.

Upsides

Acquisition of VinBrain enhances NVIDIA's AI capabilities in the healthcare sector.

Investment in Nebius Group boosts NVIDIA's AI infrastructure and cloud platform offerings.

Serve Robotics' expansion, backed by NVIDIA, highlights growth in autonomous delivery services.

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