Senior Technical Program Manager, DGX Cloud Software Products and Services

NVIDIA•Santa Clara, CA

About The Position

NVIDIA’s DGX Cloud (DGXC) supports AI research and product tasks. The Exemplar Performance Program guides cloud service provider (CSP) and NVIDIA Cloud Partner (NCP) partners toward outstanding performance, shifting the focus from GPU cost per hour to total workload cost of ownership. It provides a consistent measure of NVIDIA hardware and software performance at scale and benchmarks cloud partners. The Senior Technical Program Manager handles daily operations for the Exemplar Performance Program, translating priorities into coordinated capacity, validation, debugging, and release plans across CSPs, NCPs, and internal engineering groups. The role manages workstreams for Slurm- and Kubernetes-based training and inference benchmarking, cross-partner debugging, release and QA coordination, PLC/OSRB alignment, and program visibility. As the program expands into inference and agentic AI workloads, the role applies these methods to new stack complexities and partner engagements. The manager works directly with the program and Product lead to align execution with roadmaps and collaborates with engineering, product, and partner-facing groups across the company.

Requirements

At least 8 years of technical program management experience.
Bachelor’s degree or equivalent experience in Computer Science, Electrical Engineering, AI, or a related field.
Demonstrated ability to manage programs across multiple time zones and organizations.
Practical knowledge of inference systems, cloud platforms, and distributed computing sufficient to ask relevant technical questions and interpret engineering updates directly.
Track record managing complex, multi-stakeholder programs in fast-paced software development environments from kickoff through partner-facing delivery.
Background in software release management, QA coordination, and platform validation cycles; preferably including compliance or review routines such as open-source review, security review, or PLC/OSRB.
Strong written communication skills, including the ability to prepare concise, executive-ready status updates and blocking-issue memos.
Experience with large-scale Agile tools and reporting such as Jira, NVBugs, or equivalent, and ability to define tracking structures when needed.

Nice To Haves

Strong background in Machine Learning, Deep Learning, and Artificial Intelligence applications, with familiarity with AI benchmarking tools, evaluation frameworks, or performance scorecards for large-scale GPU workloads.
Experience with LLM inference frameworks such as vLLM, SGLang, or TensorRT-LLM, or knowledge of inference optimization methods such as speculative decoding, quantization, disaggregated prefill/decode, or multi-token prediction.
Prior experience supporting CSP or NCP partner programs, including coordinating platform validations, cluster capacity requests, or performance bring-up across organizational boundaries.
Kubernetes or cloud-native workload experience, with an understanding of how container orchestration affects GPU placement, scheduling, and workload repeatability.
Experience with open-source software release procedures, including contributor licensing, OSRB-style assessments, and ecosystem readiness.

Responsibilities

Maintain demand and allocation levels across platforms, GPU counts, topologies, and workloads; coordinate capacity reservations, access, readiness checks, and debug windows with cluster owners.
Track cross-CSP performance investigations with engineering, solutions architects, and partner teams; build playbooks and pre-flight checks for recurring issues.
Maintain a coordinated achievement and coverage plan across recipes, frameworks, results tooling, and QA; follow up on merge request reviews, merges, dependencies, and blocking regressions, and prepare release-readiness summaries for each program increment.
Connect software roadmap execution to full-stack readiness, including dependencies across infrastructure, bring-up, validation, and downstream operational support.
Standardize intake, debug handoffs, and release-evidence practices, extending them from training into inference, Kubernetes, and agentic benchmarking as priorities evolve.
Establish engineering and product rhythms for Product Life Cycle (PLC) achievements and monthly release cadences.
Identify cross-functional dependencies, mitigate risks, and drive resolution of complex technical and programmatic issues across partner, engineering, and product teams.
Develop and maintain metrics for program efficiency and delivery health; collect and analyze data to support planning and data-driven decisions, and communicate progress to senior leadership.
Lead investigations to resolution and turn technical obstacles into concise action steps.