Nvidia

Nvidia

Principal Technical Program Manager – GeForce NOW Cloud

US, CA, Santa Clara · Principal

Sponsorship not specifiedDetected 2 days ago
Distributed SystemsCloud PlatformsKubernetesCI/CDSite Reliability EngineeringMachine LearningIncident ResponseCompliancePRDsLeadershipCommunicationOrganizational Skills

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odds of building a lasting career here

42Risky
Cap-exempt (no lottery)0
Sponsors this role100
Entry-level history0
PERM / green-card track0
Lottery odds40
Fits your clock70

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Employer immigration record

from this employer's Department of Labor filings

Green-card filing pattern in this occupation

Context, not a finding about this posting: of this employer's 843 green-card filings in this occupation, 97% were for a worker who already held the job.

Green-card follow-through: 95%

Of 584 labor certifications old enough to have been used, 29 expired without the employer filing the next step. Median time from filing to decision: 497 days.97% of their filings were for a worker who already held the job.Only certifications past the 180-day window are counted — recent ones cannot have expired yet.

Files H-1B transfers

620 transfer filings in the last year, covering 1241 workers. Median labor-condition decision: 7 days. An employer that already files transfers is one that can take over an existing H-1B.

Sourced from Department of Labor LCA, PERM and prevailing-wage disclosure data. Employer matching is by name, so figures may be split across an employer's legal entities. Absence of a filing means none appears in our copy of the data, not that none exists.

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About the role

  • Communicate program health, risks, tradeoffs, and decisions to senior leadership with transparency and data-backed insights.
  • Extensive experience delivering sophisticated, multi-team programs with cross-functional collaborators.

Responsibilities

  • Own the full lifecycle of large-scale, multi-phase technical programs for both GFN and GDN - from conceptualization through requirements, develop, launch, and iteration.
  • Work closely with Engineering (infrastructure, backend, client), Product, Data Center Operations, SRE, Capacity Planning, QA, and Partner teams (games publishers and app developers).
  • Establish and maintain SLIs/SLOs/SLAs, incident response plans, and operational playbooks for a 24/7 global service.
  • Identify technical and program-level risks (capacity, latency, partner dependencies), propose mitigations, and drive decision-making with collaborators.
  • Liaise with game publishers, app creators, and external engineering teams; help integrate their build pipelines into NVIDIA's streaming infrastructure.
  • Plan and implement launches (new features, new regions, capacity expansions) with cross-team alignment; lead launch readiness reviews.
  • Drive process improvements across product delivery, streaming performance, operational efficiency, and partner workflows.
  • Excellent communication, conflict resolution, and participant management skills; ability to influence engineering, product, and business partners.
  • Strong analytical and organizational skills, with the ability to manage multiple workstreams in a fast-paced environment.
  • Experience managing global partner integrations with ISVs, OEMs, CSPs, or game publishers.

Requirements

  • Strong understanding of cloud infrastructure, distributed systems, real-time streaming services, engineering.
  • Experience with cloud-native systems (microservices, Kubernetes, observability stacks, CI/CD, API driven services).
  • Experience with operational frameworks such as SLIs/SLOs/SLAs, incident response, NPI readiness, high-level availability, and service reliability.
  • Familiarity with game streaming (GFN) or graphics delivery networks (GDN) is highly desirable.

Nice to have

  • Bachelors or Graduate degree coupled with a PMP is preferred, or equivalent experience.

Company info

  • Ways to Stand Out From the Crowd: Experience in cloud gaming, real-time media streaming, or consumer-scale cloud services.

This listing is sourced directly from Nvidia's careers page and normalized into a canonical job model.