Senior Technical Program Manager, Cloud Infrastructure

Nvidia

Confirmed live today High trust

Quick summary

Work type: On-site
Location: Santa Clara, CASeattle, WA
Salary: $168,000–$258,750 / yr
Employment: Full-time
Posted: 6 days ago
Freshness: Confirmed live today
Nearby: 99+ roles within 25 mi

Market check

Salary context

Above market

How this pay compares to similar roles

Similar $200k

This role $213k

$147k $271k

below market most similar roles pay here above market

This role pays more than 68% of similar roles. Most pay $174,950–$225,337 — the blue band above. At the midpoint, this role pays about $213k versus about $200k for comparable roles.

Based on 240 similar postings.

Employer

About Nvidia

Nvidia is a leading designer of graphics processing units (GPUs) and system-on-chip units, powering gaming, professional visualization, data centers, and artificial intelligence workloads. Industry: Semiconductors & AI Computing

Nvidia currently has 1623 open roles on FindRole.

Listed pay typically runs $184,000–$287,500 across 1110 roles with salary data.

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At a glance

TL;DR · Senior Technical Program Manager, Cloud Infrastructure

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The Senior Technical Program Manager, Cloud Infrastructure joins the DGX Cloud team to drive critical programs related to AI capacity enablement and management. This role involves partnering with internal engineering, infrastructure, and software teams to build and mature foundational capabilities for cluster bring-up, including CPU, storage, networking, and compute requirements to support GPUs. You will collaborate with Cloud Service Providers and emerging cloud providers to define workload requirements, establish milestones, and manage ongoing capacity operations. Key responsibilities include gathering technical requirements, developing roadmaps, and ensuring adherence to the Product Lifecycle process. Required skills include extensive experience in cloud infrastructure bring-up, proficiency with Jira and Smartsheet, and knowledge of Kubernetes, API integration, and Terraform. The role solves the technical challenge of scaling global AI infrastructure and managed storage solutions.

What does a Technical Program Manager earn in California?

Median $200000 from 105 postings across 25 companies.

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Skills

Cloud Infrastructure GPU Kubernetes Terraform API Integration Jira Smartsheet Product Lifecycle (PLC) Storage Networking Compute ML Infrastructure

What you'll do

Drive critical programs for AI capacity enablement and management across global cloud infrastructure.
Manage cluster bring-up processes including CPU, storage, networking, and compute requirements for GPUs.
Define and communicate workload requirements and infrastructure readiness to Cloud Service Providers (CSPs).
Develop comprehensive roadmaps, establish milestones, and ensure adherence to the Product Lifecycle process.
Manage ongoing capacity operations and engineering engagement regarding availability, maintenance, and performance indicators.
Identify and onboard third-party and in-house cloud infrastructure solutions for security, compliance, and observability.
Establish KPIs to quantitatively demonstrate program value and proactively mitigate risks to scope and schedule.
Use Jira and other management platforms to instill rigor and structure in engineering deliverables.

What we're looking for

8+ years of technical program management experience driving large-scale cloud infrastructure programs with external partners.
Extensive hands-on experience in cloud infrastructure, preferably from working at a major Cloud Service Provider (CSP).
Domain knowledge in the bring-up and end-to-end operations of compute, storage, and GPU systems.
Expert-level proficiency with Jira, Smartsheet, or similar program management tools.
BS or MS in Electrical Engineering or Computer Science, or equivalent experience.
In-depth knowledge of NVIDIA GPU products and cloud-native product and services environments (preferred).
Working knowledge of Kubernetes, API integration, Terraform, and related tooling (preferred).
Familiarity with AI and ML infrastructure and deep learning platforms (preferred).