Senior Applied Deep Learning Scientist - Large Vision Language Models

We are looking for a highly motivated Senior Applied Deep Learning Scientist with a passion for multimodal language models. Join our world-class team behind pioneering work such as Megatron-Energon, Nemotron 3 Nano Omni and our latest post-training datasets!

As a core contributor to NVIDIA's Nemotron multimodal initiative, we are pushing the frontiers of state-of-the-art open-source multimodal models. We have a unique perspective in that we strive for open models, open weights, open data. Our mission is straightforward: create models that perform exceptionally in real-world applications right out of the box, while empowering and advancing the broader multimodal LLM ecosystem.

Responsibilities:

• Push the boundaries of the NVIDIA Nemotron Omni family of models to enable powerful downstream applications, including document intelligence, mathematical reasoning, multi-turn multimodal dialogue systems, and advanced software & agentic assistants. The role spans the full pipeline, from pre-training through post-training.

• Help us prepare large-scale multimodal datasets to train cutting-edge foundation models across text, image, audio and video. This includes developing robust data processing pipelines to curate high-quality training data, augmenting it, synthetically generating labels and providing the infrastructure to load and serve data in real time.

• Collaborate globally with other team members, researchers and developers from different departments at NVIDIA and AI startups we work with, to turn research and innovations into impactful products.

Requirements:

• M.Sc. or Ph.D. in Computer science (or a related field), or equivalent research experience in LLMs, systems, or connected areas.

• 10+ years of industry experience in computer vision, including designing data pipelines for diverse data modalities and deploying models from research into production.

• Strong understanding of the theoretical foundations of LLMs/VLMs and familiarity with the latest academic developments in the field.

• Solid hands-on coding skills with PyTorch and Python, experience with multi-GPU training on large-scale compute clusters, fluency with Docker, and Linux systems expertise.

Ways to stand out from the crowd:

• Contributions to open-source LLM systems or large-scale AI infrastructure.

• Previous AI-related projects or entrepreneurial experience in a closely connected domain.

• An academic track record of publications in deep learning.