Senior Technical Program Manager, DGX Cloud Software Products and Services

NVIDIA•Santa Clara, CA

About The Position

NVIDIA is seeking a Senior Technical Program Manager to manage daily operations for the DGX Cloud Exemplar Performance Program. The program guides cloud service provider (CSP) and NVIDIA Cloud Partner (NCP) partners toward strong performance and measures the performance of NVIDIA hardware and software at scale, emphasizing total workload cost of ownership. The role translates priorities into coordinated capacity, validation, debugging, and release plans across partners and internal engineering teams. Workstreams include Slurm- and Kubernetes-based training and inference benchmarking, cross-partner debugging, release and QA coordination, Product Life Cycle (PLC) and Open Source Review Board (OSRB) alignment, and program visibility. As the program expands into inference and agentic AI workloads, the role will apply its operating methods to new technical requirements and partner engagements. The position works directly with the program and Product lead to align execution with roadmaps and requires strong technical judgment and collaboration across engineering, product, and partner-facing groups.

Requirements

At least 8 years of technical program management experience.
Bachelor’s degree in Computer Science, Electrical Engineering, AI, or a related field, or equivalent experience.
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 write concise, executive-ready program 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 none exist.

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 platform validations, cluster capacity requests, or performance bring-up across organizational boundaries.
Kubernetes or cloud-native workload experience, including understanding 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 debugging windows with cluster owners.
Track cross-CSP performance investigations with engineering, solutions architects, and partner teams; develop playbooks and pre-flight checks for recurring issues.
Maintain coordinated achievement and coverage plans across recipes, frameworks, results tooling, and QA; follow up on merge request reviews, merges, dependencies, and blocking regressions.
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, debugging handoffs, and release evidence practices, extending them from training to inference, Kubernetes, and agentic benchmarking as priorities evolve.
Establish engineering and product rhythms for Product Life Cycle 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 program efficiency and delivery-health metrics; collect and analyze data to support planning and data-driven decisions.
Communicate program progress clearly to senior leadership.