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Senior Manager, Software Engineering - RL Post-Training Frameworks
NVIDIA
Santa Clara, USA
Full-time
Senior Level
Hybrid
USD 272000-431250 yearly
Discovered 1 weeks ago
Free
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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.
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