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Senior Research Scientist, Post-Training LLM and DLM
Senior Research Scientist, Post-Training LLM and DLM
NVIDIA is looking for a Senior Research Scientist to design and implement post-training algorithms for Large Language Models (LLMs) and Diffusion Language Models (DLMs). This role involves driving efficiency in training pipelines, improving serving systems, and contributing to open-source communities in the field of large-scale generative AI.
About the role
About the Role
NVIDIA is seeking a Senior Research Scientist passionate about Large Language Model (LLM) and Diffusion Language Model (DLM) post-training and system optimization. This role is crucial for shaping the future of large-scale generative AI. NVIDIA is a leader in foundation models and generative AI systems, facilitating cutting-edge research and real-world deployment at an unprecedented scale. The team focuses on advancing post-training algorithms, building efficient large-scale systems, and developing robust evaluation frameworks to ensure reliability and scalability. Join a team of world-class researchers and engineers to build the next generation of AI.
What you will be doing:
- Designing and implementing post-training algorithms for LLMs and DLMs.
- Driving efficiency and scalability improvements across training pipelines and serving systems.
- Collaborating with researchers to translate cutting-edge ideas into production-ready implementations.
- Exploring new paradigms for evaluation.
- Demonstrating strong engineering practices and contributing to open-source communities.
What we need to see:
- PhD in Computer Science, Electrical Engineering, or a related field, or equivalent research experience in LLMs, systems, or related areas.
- 2+ years of experience in machine learning, systems, distributed computing, or large-scale model training.
- Proficiency in Python with hands-on experience in frameworks such as PyTorch.
- Solid background in computer science fundamentals: algorithms, data structures, parallel/distributed computing, and systems programming.
- Proven ability to collaborate across research and engineering teams in multifaceted environments.
Ways to stand out from the crowd:
- Expertise in post-training LLMs with novel algorithmic/data pipelines.
- Experience developing and scaling large distributed systems for deep learning.
- Contributions to open-source LLM systems or large-scale AI infrastructure.