Used Tools & Technologies
GenAIRequired Skills & Competences
Tag name is followed by "@" symbol and proficiency level value.
About proficiency levels:
- 1-2 — basic awareness. Minimal hands-on experience, and a rudimentary understanding of the technology's purpose;
- 3-6 — daily use. Comfortable and regular usage, capable of handling common tasks and challenges related to the technology;
- 7-9 — you are an expert, you can teach others, you know all the pitfalls and tricks;
- 10 — exceptional knowledge, comprehensive understanding, and adeptness in all aspects of the technology, including advanced problem-solving. Think twice before claiming or demanding such level.
Machine Learning @ 4
Experimentation @ 4
LLM @ 4
Generative AI @ 4
AI @ 4
Reinforcement Learning @ 6
vLLM @ 4
SGLang @ 4
- 1-2 — basic awareness. Minimal hands-on experience, and a rudimentary understanding of the technology's purpose;
- 3-6 — daily use. Comfortable and regular usage, capable of handling common tasks and challenges related to the technology;
- 7-9 — you are an expert, you can teach others, you know all the pitfalls and tricks;
- 10 — exceptional knowledge, comprehensive understanding, and adeptness in all aspects of the technology, including advanced problem-solving. Think twice before claiming or demanding such level.
Details
Join NVIDIA and help build the Nemotron models that will define the foundation of open-source generative AI. You will work at the intersection of research and engineering to invent, implement, and scale the core post-training technologies behind Nemotron models, contributing to open-source software and large-scale production pipelines.
Responsibilities
- Contribute as a core member of Nemotron models post-training, covering: synthetic data and algorithmic research for agentic RL; data and training infrastructure implementation; vendor data acquisition and experimentation; and large-scale research & production model post-training.
- Advance open-source foundation models by developing training data, benchmarks, LLMs and software (including NeMo-RL, Nemo-Gym and related tooling).
- Solve end-to-end foundation model post-training challenges across the model lifecycle: orchestration, data pre-processing, model training and tuning, and model deployment.
- Publish and present results at academic and industry conferences.
Requirements
- Master or PhD degree in computer science, machine learning or other quantitative domains (or equivalent experience).
- 5+ years of working or research experience in model mid-training / post-training, reinforcement learning and agentic systems.
- Hands-on experience in data curation and model training for agentic and reasoning capabilities.
- In-depth experience using or developing inference and deployment environments such as vLLM, SGLang, or TRT-LLM.
Ways to Stand Out
- Industrial experience in reinforcement learning for leading foundation models.
- Experience in optimizing model quality from real-world traffic feedback.
Compensation & Benefits
- Base salary range (Level 4): 192,000 USD - 304,750 USD.
- Base salary range (Level 5): 224,000 USD - 356,500 USD.
- Eligible for equity and company benefits.
Additional Information
- Applications for this job will be accepted at least until June 20, 2026.
- NVIDIA uses AI tools in its recruiting processes.
- NVIDIA is an equal opportunity employer and committed to fostering an inclusive work environment.