Senior RL Post-Training Frameworks Architect

NVIDIA; United States; On-site; USD 180,000 - 260,000; Full time

Job summary

NVIDIA is seeking an RL post-training infrastructure engineer to design and scale rollout loops for AI research and production workloads. You will optimize RL training, inference, and rollout across GPU, CPU, and LPU resources while improving open-source frameworks and runtimes.

You will collaborate with researchers, labs, and hardware teams to drive resilient, scalable systems and contribute to VeRL, Miles, TorchTitan, Ray, and Monarch in a frontier AI environment.

Qualifications

• MS or PhD in Computer Science, Computer Engineering, or a related field (or equivalent experience).
• 5+ years of professional experience in distributed systems, HPC, DL infrastructure, or ML systems engineering.
• Strong proficiency in Python and C/C++.
• Experience building or contributing to large-scale distributed systems or runtime frameworks in production.
• Strong communication skills and collaboration across teams.
• Deep expertise in RL for LLM post-training, PyTorch internals, Kubernetes runtimes, and end-to-end distributed design.

Responsibilities

• Architect and build RL post-training infrastructure that scales from single-GPU experiments to production across thousands of nodes.
• Tune training-inference-rollout loops on GPUs/CPUs/LPUs for performance and contribute to RL frameworks.
• Ensure fault tolerance, elastic scaling, and fast restarts for long-running distributed jobs.
• Collaborate with researchers, hardware teams, and vendors to prioritize capabilities and improve runtimes.

Skills

Python

C/C++

Distributed systems

PyTorch

Kubernetes

Ray

Monarch

Education

MS or PhD in CS/CE or related field

Tools

Ray

Monarch

TensorRT-LLM

Megatron-LM

NCCL

NVLink

InfiniBand

Job description

NVIDIA is seeking an RL post-training infrastructure engineer to design and scale rollout loops for AI research and production workloads. You will optimize RL training, inference, and rollout across GPU, CPU, and LPU resources while improving open-source frameworks and runtimes.

You will collaborate with researchers, labs, and hardware teams to drive resilient, scalable systems and contribute to VeRL, Miles, TorchTitan, Ray, and Monarch in a frontier AI environment.