Senior Manager, Software Engineering - RL Post-Training Frameworks

NVIDIA; United States; On-site; USD 250,000 - 360,000; Full time

Job summary

NVIDIA is seeking a Senior Software Engineering Manager to lead the RL post-training frameworks effort, spanning VeRL, Miles, Slime, SkyRL, TorchTitan, and related stacks. You will guide architecture, performance, and upstream collaboration, while building a high-impact team across US/APAC.

Lead multi-organization execution, mentor engineers, and drive durable upstream improvements for scalable RL workloads on NVIDIA platforms.

Qualifications

• MS/PhD in CS/CE or equivalent experience.
• 10+ years in distributed systems or AI infra; 4+ years as manager.
• Ability to shape technical strategy and multi-team plans.
• Experience collaborating with open-source and external partners.

Responsibilities

• Define RL post-training framework strategy and priorities.
• Build and mentor teams across US/APAC.
• Drive multi-team execution with measurable outcomes.
• Partner with product, research, CUDA, and open-source communities.

Skills

Distributed systems

Engineering management

AI frameworks

Open-source collaboration

Strategic planning

Education

MS/PhD in Computer Science or Computer Engineering

Tools

Kubernetes

Megatron-Core

Ray

NIXL

Job description

Can you bring together globally distributed teams and the systems they build into a production-quality reinforcement learning ecosystem for researchers and model builders? Reinforcement learning post-training is where modern AI systems learn to reason, use tools, follow detailed instructions, and act as agents. Making that capability work at scale creates one of the most demanding systems problems in AI: a single RL run ties together inference, rollout, reward and critic evaluation, and training. At frontier scale, these loops have to run reliably across GPUs, CPUs, networking, storage, and open-source runtimes. You will lead the work to build, extend, and harden the rapidly evolving pieces to compose cleanly and scale with the most ambitious RL projects on NVIDIA’s platforms.

To meet that challenge, NVIDIA is building an RL Frameworks engineering team for the open-source tools and infrastructure that researchers, model builders, and external partners depend on. We are looking for a Senior Software Engineering Manager to set strategy, build the team, and convert emerging technical, customer, and partner signals into clear engineering priorities. The role spans RL frameworks such as VeRL, Miles, Slime, SkyRL, TorchTitan, and related post-training stacks, along with the systems those stacks build on and compose with: Megatron-Core, Ray, Monarch, NIXL, SGLang, Kubernetes, and NVIDIA platform libraries. Come build the ecosystem that the next generation of AI will rely on!

What you will be doing:

You will own NVIDIA’s RL post-training frameworks strategy: where we invest directly, where we partner upstream, and how we prioritize based on customer impact, ecosystem leverage, technical feasibility, and opportunity cost. This is senior technical leadership work: using systems depth to evaluate architecture and performance claims across training, inference, rollout, orchestration, and the NVIDIA platform. You will help expert teams converge on integrations that improve RL framework quality and user value, then turn those decisions into measurable execution plans. The work includes benchmarking and reproducibility criteria, delivery across open-source frameworks and distributed runtimes, and close partnership with product management, research, DevRel, customer-facing teams, hardware, CUDA, networking, math libraries, compilers, and external open-source collaborators.

You will also build the team: recruiting and developing managers and senior ICs, creating an effective US/APAC operating model, reviewing capacity against commitments, and setting clear ownership and decision rights. You will coach engineers to contribute credibly in open-source ecosystems and carry NVIDIA’s priorities through high-quality upstream work. Because the technical work crosses organizations by design, you will turn open technical and partner questions into concrete and measurable action, set delivery goals, and hold the quality bar. Success means validated, valuable work rather than work that merely lands, plus durable open-source improvements that make RL workloads run well on NVIDIA systems.

What we need to see:

• MS or PhD in Computer Science, Computer Engineering, or a related field (or equivalent experience)
• 10+years of software engineering experience in distributed systems, AI frameworks, ML infrastructure, high-performance computing, or systems software, with4+years as an engineering manager for software teams
• Strong technical background in distributed AI systems, including the ability to reason across training, inference, orchestration, and end-to-end performance, and challenge architecture and performance tradeoffs with senior engineers
• Experience defining domain-level technical strategy, making build-vs-buy or upstream-vs-internal investment decisions, and creating multi-team execution plans
• Ability to drive engineering work across organizational boundaries, influence without direct authority, and communicate tradeoffs clearly to senior leaders and executives
• Experience hiring and leading engineering teams, developing technical leaders or new managers, and creating staffing plans for constantly evolving technical domains
• Experience establishing workflows, success criteria, metrics, or decision gates that improve engineering execution across teams
• Background collaborating with open-source communities, research teams, external partners, or customer-facing teams

Ways to stand out from the crowd:

• Hands-on experience with RL post-training frameworks or algorithms such as RLHF, PPO, GRPO, DPO, reward modeling, VeRL, Miles, Slime, SkyRL, OpenRLHF, NeMo-Aligner, or TorchTitan
• Background with runtime and orchestration systems such as Ray, Monarch, Kubernetes, Slurm, or comparable actor- and task-based systems
• Experience scaling workloads across thousands of GPUs or heterogeneous systems, including fault tolerance, elastic recovery, stragglers, resource contention, or benchmark reproducibility
• Familiarity with NVIDIA platform