LLM Reinforcement Learning Framework Engineer
NVIDIA· Architect
Shanghaifull-timePosted 6mo ago
About the role
Role focused on advancing large language model (LLM) capabilities through reinforcement learning, improving reasoning for math, coding, and agentic AI, and integrating RL components into NVIDIA’s LLM training and serving stack.
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.
Qualifications
- 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.
- Understanding of GPU architecture and performance optimization is a strong plus.
- Strong problem‑solving, debugging, and collaboration skills, with a passion for innovation and delivering industry‑leading AI solutions.
Skills
Required:PythonPyTorchReinforcement Learning for LLMsAsync/Distributed Orchestration
Nice to have:LLM frameworks
Benefits
- Highly competitive salaries
- Comprehensive benefits package
- Benefits accessible at www.nvidiabenefits.com/
Last updated Jul 3, 2026. Interested? Apply on the company site.
About NVIDIA
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