LLM Reinforcement Learning Framework Engineer

Responsibilities

• Developing and deploying reinforcement learning algorithms for LLM post‐training to improve reasoning and alignment.
• Integrating RL components into NVIDIA’s LLM training and serving stack with a cross‐functional team of engineers and researchers.
• Crafting and running experiments, evaluations, and debugging workflows to ensure robustness, scalability, and reliability in production.
• What we need to see
• Strong Python programming skills with production‐quality PyTorch experience in multi‐GPU and distributed training environments.
• Hands‐on experience with modern LLM frameworks such as NeMo RL, Megatron‐LM, DeepSpeed, vLLM, TensorRT‐LLM, or similar is a big plus.
• Practical experience with reinforcement learning applied to LLMs or large‐scale sequence models.
• Familiarity with async and distributed orchestration (e.g., asyncio, torch.distributed, Ray, or equivalent).
• 3+ years of relevant industry or research experience, and BS/MS (or equivalent) in CS, CE, EE, or a related field.
• Solid foundations in probability, optimization, statistics, and deep learning.

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