Senior Manager, Software Engineering - RL Post-Training Frameworks

NVIDIA•Santa Clara, WA

9d•$272,000 - $431,250•Hybrid

About The Position

NVIDIA is building an RL Frameworks engineering team to develop and maintain open-source tools and infrastructure for researchers, model builders, and external partners. The Senior Manager, Software Engineering will lead NVIDIA’s reinforcement learning post-training frameworks strategy, build the team, and translate technical, customer, and partner signals into engineering priorities. The role covers frameworks such as VeRL, Miles, Slime, SkyRL, and TorchTitan, as well as systems they integrate with, including Megatron-Core, Ray, Monarch, NIXL, SGLang, Kubernetes, and NVIDIA platform libraries. The role involves deciding where NVIDIA should invest directly, partner upstream, and prioritize work based on customer impact, ecosystem leverage, technical feasibility, and opportunity cost. The manager will evaluate architecture and performance across training, inference, rollout, orchestration, and the NVIDIA platform; guide teams toward useful integrations; and establish measurable execution plans, benchmarking, and reproducibility criteria. The position works closely with product management, research, DevRel, customer-facing teams, hardware, CUDA, networking, math libraries, compilers, and external open-source collaborators. The manager will recruit and develop managers and senior individual contributors, establish an effective US/APAC operating model, review capacity against commitments, and define ownership and decision rights. They will coach engineers on effective open-source contributions, set delivery goals and quality standards, and drive cross-organizational work toward validated outcomes and durable open-source improvements that enable RL workloads to 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.
4+ years as an engineering manager for software teams.
Strong technical background in distributed AI systems, with the ability to reason across training, inference, orchestration, and end-to-end performance, and to challenge architecture and performance tradeoffs with senior engineers.
Experience defining domain-level technical strategy, making build-versus-buy or upstream-versus-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 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.

Nice To Haves

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.

Responsibilities

Own NVIDIA’s strategy for RL post-training frameworks, including direct investment, upstream partnerships, and prioritization.
Evaluate architecture and performance claims across training, inference, rollout, orchestration, and the NVIDIA platform.
Guide teams toward integrations that improve RL framework quality and user value, and translate decisions into measurable execution plans.
Establish benchmarking and reproducibility criteria and oversee delivery across open-source frameworks and distributed runtimes.
Partner with product management, research, DevRel, customer-facing teams, hardware, CUDA, networking, math libraries, compilers, and external open-source collaborators.
Recruit and develop managers and senior individual contributors; build an effective US/APAC operating model.
Review team capacity against commitments and establish clear ownership and decision rights.
Coach engineers to contribute effectively to open-source ecosystems and represent NVIDIA’s priorities through high-quality upstream work.
Turn technical and partner questions into concrete, measurable actions; set delivery goals and maintain a high quality bar.
Drive validated, valuable work and durable open-source improvements for RL workloads on NVIDIA systems.