Senior Software Engineer, RL Post-Training Frameworks

About this role

Reinforcement learning post-training is driving some of the most significant capability gains in AI today. It is the process that teaches a model to reason through hard problems, follow complex instructions, and act as an autonomous agent. It is also one of the hardest infrastructure challenges in the field. RL requires inference, rollout generation, and training running in a continuous loop. The rollout step is what makes it hard: the model must interact with environments, tools, and other models to produce the signal that drives learning. Coordinating actor, critic, and reward models across heterogeneous hardware at scale pushes the limits of what distributed systems can do.

NVIDIA is building an RL Frameworks engineering team to develop the open-source tools and infrastructure that AI researchers and post-training teams depend on. The team spans the full software stack, from collaborating closely with the researchers and labs pushing the frontier, to contributing to RL frameworks like [VeRL](https://github.com/volcengine/verl), [Miles](https://github.com/radixark/miles), and [TorchTitan](https://github.com/pytorch/torchtitan), to improving the distributed runtimes they depend on, including [Ray](https://github.com/ray-project/ray) and [Monarch](https://github.com/meta-pytorch/monarch). Whether your strength is working with researchers to understand and address their need optimizing deep learning frameworks, or building distributed infrastructure, we want to hear from you. Come join us to build the systems that enable the next generation of AI.

What you will be doing:

You will architect and build RL post-training infrastructure that scales efficiently from experimentation on a single GPU to production across thousands of nodes. This means tuning RL training-inference-rollout loops on GPUs, CPUs, and LPUs for performance where it matters, contributing to and improving the performance and usability of open-source RL frameworks, and partnering with the teams w...