Senior Manager, Software Engineering - RL Post-Training Frameworks
NVIDIA· Mgmt, Dev Tech SW
Hybrid (US, CA, Santa Clara), Hybrid (US, DC, Remote), Hybrid (US, NY, Remote), Hybrid (US, WA, Remote), Hybrid (US, CA, Remote), Hybrid (US, MA, Remote)$272k - $431.3kfull-timePosted 1mo ago
About the role
Lead strategy and build the RL post-training frameworks ecosystem and team, coordinating across distributed teams and NVIDIA platform components to enable scalable RL post-training tooling and infrastructure.
Responsibilities
- Own NVIDIA's RL post-training frameworks strategy: investments, upstream partnerships, and prioritization based on customer impact, ecosystem leverage, feasibility, and opportunity cost.
- Evaluate architecture and performance across training, inference, rollout, orchestration, and the NVIDIA platform; converge on integrations to improve framework quality and user value; translate decisions into measurable execution plans.
- Benchmarking and reproducibility criteria; deliver across open-source frameworks and distributed runtimes; partner with product management, research, DevRel, customer-facing teams, hardware, CUDA, networking, math libraries, compilers, and open-source collaborators.
- Build and develop the team: recruiting and developing managers and senior ICs; establish US/APAC operating model; review capacity; set ownership and decision rights.
- Coach engineers for credible contributions in open-source ecosystems and ensure upstream work aligns with NVIDIA priorities; translate cross-organizational questions into concrete actions, goals, and quality standards.
- Set delivery goals, maintain quality bar, and drive durable open-source improvements that enable RL workloads on NVIDIA systems.
Qualifications
- MS or PhD in Computer Science, Computer Engineering, or related field (or equivalent experience)
- 10+ years of software engineering in distributed systems, AI frameworks, ML infrastructure, HPC, or systems software; 4+ years as an engineering manager
- Strong technical background in distributed AI systems; ability to reason across training, inference, orchestration, and end-to-end performance
- Experience defining domain-level technical strategy, build-vs-buy or upstream-vs-internal investment decisions, and multi-team execution plans
- Ability to influence across organizational boundaries, communicate tradeoffs to senior leaders
- Experience hiring and leading engineering teams, developing technical leaders, and staffing evolving domains
- Experience establishing workflows, success criteria, metrics, or decision gates for engineering execution
- Background collaborating with open-source communities, research teams, external partners, or customer-facing teams
Skills
Required:Distributed AI systemsTechnical strategy and orchestration
Benefits
- equity
- benefits (see NVIDIA benefits page)
Last updated Aug 28, 2026. Interested? Apply on the company site.
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