This role pays more than
90%
of similar roles. Most pay
$178,537–$235,750
— the shaded band above.
At the midpoint, this role pays about
$268k
versus about
$207k
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
896 open roles
on FindRole.
Listed pay typically runs
$184,000–$287,500
across 876 roles with salary data.
Senior Technical Product Manager – DGX Enterprise Infrastructure and Cloud-Native Operations will join the team to architect the operational future of enterprise AI. This role focuses on creating a software-defined blueprint that transforms raw DGX hardware into high-availability, self-healing infrastructure for large-scale private clusters. You will define the product lifecycle from bare-metal provisioning and network fabric configuration to automated firmware rollouts and telemetry diagnostics. Key responsibilities include integrating DGX systems into cloud-native ecosystems using Kubernetes operators, container orchestration, and GPU partitioning. The role requires expertise in data center networking including InfiniBand and Ethernet, storage architectures, and infrastructure-as-code tools like Ansible, Terraform, or Pulumi. You will solve the challenge of making on-premise environments feel as fluid as public clouds by building APIs and services to ensure standardized, repeatable, and resilient deployments for enterprise AI factories.
What does a Technical Product Manager earn?
Median $182500 from 33 postings across 16 companies.
Artificial Intelligence
Machine Learning
Deep Learning
Kubernetes
Cloud Infrastructure
Distributed Systems
Linux
SDN
Agile
Software Development Lifecycle
System Integration
Bare Metal
open-source
GPU
Large Language Models
Machine Learning
Artificial Intelligence
High-Performance Computing
Cloud Computing
Datacenter Workloads
Training and Inference
Computing Architectures