Senior Manager, Software Engineering - RL Post-Training Frameworks

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.

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.

• 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, with 4+ 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

• 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 components such as CUDA, NCCL, cuDNN, TensorRT-LLM, Transformer Engine, Nsight, NeMo, or Megatron-Core

• Demonstrated ability to turn customer or partner needs into reusable upstream improvements rather than one-off support

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This posting is for an existing vacancy. 

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