Senior Technical Program Manager, GenAI and Models
Location
California
Posted
2 days ago
Salary
$168K - $258.8K / year
Seniority
Senior
Job Description
Senior Technical Program Manager, GenAI and Models
NVIDIA
• Lead multi-functional programs across training frameworks, evaluation environments, agent and model runtimes, datasets, verifiers, and distributed training infrastructure. • Partner with AI researchers, engineering leaders, product, infrastructure, and QA teams to define roadmaps, achievements, release plans, and measurable success criteria. • Coordinate large-scale RL training and evaluation experiments, including handling GPU resources, dependency tracking, run scheduling, results reporting, release readiness, technical decisions, integration plans, and program updates.
Job Requirements
- Bachelor’s degree in computer science, engineering, or a related technical field, or equivalent experience.
- 10+ years of technical program management, engineering program management, or related experience delivering sophisticated software platforms.
- Experience leading global, matrixed programs across research, software engineering, infrastructure, QA, release teams, and partner groups.
- Strong understanding of the AI model lifecycle, including training, post-training, evaluation, experimentation, production readiness, reinforcement learning concepts, and GPU-accelerated distributed systems.
- Experience running software releases across repositories, dependencies, test configurations, quality gates, collaborator approvals, open-source workflows, CI/CD systems, and tools such as GitHub, Git, Jira, Linear, Aha!, or Confluence.
Benefits
- equity
- benefits
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• Lead AI initiatives spanning research, software, hardware, infrastructure, product, quality, security, legal, operations, marketing, and developer relations. • Build roadmaps, achievements, ownership models, governance plans, risk tracking, and success metrics. • Partner with technical teams to align model development, training, inference, evaluation, GPU capacity, and production deployment. • Support architecture and integration decisions while resolving cross-team dependencies. • Share clear updates with leaders on progress, tradeoffs, risks, and recommendations.
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