How do bold AI research ideas become diligent training, evaluation, and production systems? NVIDIA’s Deep Learning Software team is looking for a Senior Technical Program Manager to lead programs across model pre-training, production RL runs, evaluation, and agentic AI infrastructure. We build the software foundations that help research and engineering teams train, evaluate, and deliver sophisticated AI models. We partner across research, platform engineering, distributed computing, evaluation, and open-source development to turn sophisticated technical goals into clear software plans. Come help us improve how sophisticated AI systems are built and validated!
What you'll be doing:
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.
What we need to see:
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.
Ways to stand out from the crowd:
Experience supporting reinforcement learning, post-training, agentic AI, or large-scale model evaluation programs.
Familiarity with PPO, GRPO, asynchronous RL, distributed inference, rollout generation, policy optimization, evaluation harnesses, verifiers, benchmark development, or reproducible experimentation.
Knowledge of GPU infrastructure, distributed training, Kubernetes, workload schedulers, cluster capacity management, performance analysis, open-source contributor workflows, release readiness, or operational metrics.
You will also be eligible for equity and benefits.
This posting is for an existing vacancy.
NVIDIA uses AI tools in its recruiting processes.
NVIDIA is committed to fostering an inclusive work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.#deeplearningSkills Required
- Bachelor's degree in computer science, engineering, or 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
- Experience coordinating large-scale RL training and evaluation experiments, including handling GPU resources, dependency tracking, run scheduling, results reporting, and release readiness
- Experience supporting reinforcement learning, post-training, agentic AI, or large-scale model evaluation programs
- Familiarity with PPO, GRPO, asynchronous RL, distributed inference, rollout generation, policy optimization, evaluation harnesses, verifiers, benchmark development, or reproducible experimentation
- Knowledge of GPU infrastructure, distributed training, Kubernetes, workload schedulers, cluster capacity management, performance analysis, open-source contributor workflows, release readiness, or operational metrics
NVIDIA Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about NVIDIA and has not been reviewed or approved by NVIDIA.
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Equity Value & Accessibility — Equity awards and a discounted ESPP are highlighted as core parts of total compensation, enabling employees to share in the company’s success. Stock-based compensation and the two-year lookback ESPP are consistently described as especially valuable.
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Healthcare Strength — Health coverage is portrayed as robust, with comprehensive medical, dental, and vision options alongside mental health support and on-site care resources. Employer HSA contributions and wellness perks reinforce the depth of the offering.
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Retirement Support — Retirement programs are depicted as strong, featuring a meaningful 401(k) match with Roth options and support for Mega Backdoor Roth contributions. These elements position long-term savings as a notable advantage of the total rewards package.
NVIDIA Insights
What We Do
NVIDIA’s invention of the GPU in 1999 sparked the growth of the PC gaming market, redefined modern computer graphics, and revolutionized parallel computing. More recently, GPU deep learning ignited modern AI — the next era of computing — with the GPU acting as the brain of computers, robots, and self-driving cars that can perceive and understand the world. Today, NVIDIA is increasingly known as “the AI computing company.”
