Senior Research Scientist, Post-training LLM and Dlm

NVIDIA NVIDIA · Semiconductors · Santa Clara, CA +1 · Remote

Senior Research Scientist focused on post-training algorithms for LLMs and DLMs, system optimization for training and serving, and developing evaluation frameworks. The role involves translating research ideas into production-ready implementations and contributing to open-source communities.

What you'd actually do

  1. Designing and implementing post-training algorithms LLMs and DLMs.
  2. Driving efficiency and scalability improvements across training pipelines and serving systems
  3. Collaborating with researchers to translate cutting-edge ideas into production-ready implementations.
  4. Exploring new paradigms for evaluation.
  5. Demonstrating strong engineering practices, and contributing to open-source communities.

Skills

Required

  • PhD in Computer Science, Electrical Engineering, or related field, or equivalent research experience in LLMs, systems, or related areas.
  • 2+ years of experiences 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.

Nice to have

  • 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.

What the JD emphasized

  • post-training LLMs with novel algorithmic/data pipelines
  • developing and scaling large distributed systems for deep learning
  • contributions to open-source LLM systems or large-scale AI infrastructure

Other signals

  • post-training algorithms
  • system optimization
  • large-scale generative AI
  • foundation models
  • evaluation frameworks