Senior Manager, Software Engineering - RL Post-Training Frameworks

NVIDIA is seeking a Senior Software Engineering Manager to lead its RL Frameworks engineering team. The role will define strategy for reinforcement learning post-training frameworks, oversee distributed systems and open-source infrastructure, build and develop engineering teams, and drive cross-organizational execution and ecosystem collaboration.

Responsibilities:

• 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

Requirements:

• 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