Senior Research Scientist, Post-Training LLM and DLM

Nvidia

Remote

Quick summary

Work type: Remote
Location: Santa Clara, CA
Salary: $168,000–$264,500 / yr
Posted: 25 days ago

Market check

Salary context

Above market

How this pay compares to similar roles

Similar $194k

This role $216k

$131k most similar roles pay here $279k

This role pays more than 71% of similar roles. Most pay $161,500–$226,600 — the shaded band above. At the midpoint, this role pays about $216k versus about $194k for comparable roles.

Based on 240 similar postings.

Employer

About Nvidia

Nvidia is a leading designer of graphics processing units (GPUs) and system-on-chip units, powering gaming, professional visualization, data centers, and artificial intelligence workloads. Industry: Semiconductors & AI Computing

Nvidia currently has 563 open roles on FindRole.

Listed pay typically runs $168,000–$264,500 across 556 roles with salary data.

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View all roles at Nvidia

At a glance

TL;DR · Senior Research Scientist, Post-Training LLM and DLM

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Join NVIDIA as a Senior Research Scientist focused on advancing Large Language Models (LLMs) and Diffusion Language Models (DLMs), where you will design and implement post-training algorithms to enhance efficiency and scalability in training pipelines and serving systems. You’ll collaborate with top researchers to develop production-ready implementations of cutting-edge ideas, explore new evaluation paradigms, and contribute to open-source communities. Ideal candidates hold a PhD or equivalent experience in machine learning, systems, or related fields, with proficiency in Python and frameworks like PyTorch, alongside expertise in distributed computing and large-scale model training. Experience with post-training LLMs, scaling distributed deep learning systems, and contributions to open-source AI infrastructure are highly valued as you tackle complex challenges at the forefront of generative AI research and deployment.

Skills

Python PyTorch LLMs DLMs Post-training algorithms Distributed computing Large-scale model training Open-source contributions CI/CD Git Linux CUDA NVIDIA GPUs Scalable systems Evaluation frameworks

What you'll do

Design and implement post-training algorithms for LLMs and DLMs.
Drive efficiency improvements in training pipelines and serving systems.
Translate cutting-edge research ideas into production-ready implementations.
Explore new paradigms for evaluating large-scale AI models.
Contribute to open-source communities with innovative solutions.

What we're looking for

PhD in Computer Science, Electrical Engineering, or related field with relevant research experience.
Over 2 years of experience in machine learning, systems, distributed computing, or large-scale model training.
Proficiency in Python and hands-on experience with PyTorch framework.
Strong background in computer science fundamentals including algorithms, data structures, parallel/distributed computing, and systems programming.
Expertise in designing and implementing post-training algorithms for LLMs and DLMs.
Experience developing and scaling large distributed systems for deep learning.
Contributions to open-source projects related to LLM systems or large-scale AI infrastructure.

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