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Nvidia Senior Applied Research Scientist, Multimodal Retrieval in Remote, California

NVIDIA’s Retriever team is seeking a Senior Applied Research Scientist with experience researching, developing, and deploying deep learning models at scale across a range of modalities. You’ll join a team of Applied Research Scientists, Machine Learning and MLOps Engineers working on the next generation of retrieval pipelines for RAG, with a focus on modalities beyond text.

At NVIDIA we’re building the framework upon which production RAG systems are based. We have contributed to top research models (https://huggingface.co/nvidia/NV-Embed-v2) in the text embedding space, topping the MTEB leaderboard (https://huggingface.co/spaces/mteb/leaderboard) and have developed commercially viable versions of these models (https://build.nvidia.com/nvidia/nv-embedqa-mistral-7b-v2) for use in production systems by our customers. Come be a part of our world-class team building the future of Retrieval.

What you’ll be doing:

  • Working with our team of researchers to develop efficient and performant models and pipelines that extract text content from images, video, audio and other modalities.

  • Exploring and crafting datasets, metrics, experiments, and validation scripts to develop standard methodologies for research. These methodologies will offer customers clear guidance on which models and pipelines to apply in specific contexts.

  • Helping ML Engineers scale pipelines to production capability through the development of NVIDIA Inference Microservices (https://www.nvidia.com/en-us/ai/) (NIMs) and blueprints (https://nvidianews.nvidia.com/news/nvidia-and-global-partners-launch-nim-agent-blueprints-for-enterprises-to-make-their-own-ai) which demonstrate how to deploy NIMs in a pipeline effectively.

  • Writing papers, blog posts, documentation and trainings that help customers understand and take advantage of our research.

  • Keeping up to date with the latest developments in Retrieval across academia and industry.

What we need to see:

  • Candidates with a Master's, Ph.D. or equivalent experience in retrieval or multimodal research are preferred, along with a track record of publication in leading conferences like SIGIR, KDD, UMAP, RecSys, etc.

  • An understanding of the state of the art in retrieval research, with a focus on multimodal content retrieval.

  • 3+ years of experience developing multimodal systems across a range of models and platforms. Information retrieval experience is a big plus.

  • Knowledge of best practices in batching, streaming, and scaling of ingestion pipelines to support real-world applications.

  • Excellent Python programming skills and a strong understanding of the Python deep learning ecosystem (PyTorch, Tensorflow, MXNet, etc).

  • An ability to share and communicate your ideas clearly through blog posts, papers, kernels, GitHub, etc.

  • Excellent communication and interpersonal skills are required, along with the ability to work in a dynamic, product-oriented, distributed team. A history of mentoring junior engineers and interns is a plus.

With a competitive salary package and benefits, NVIDIA is widely considered to be one of the technology world’s most desirable employers. We have some of the most forward-thinking and hardworking people in the world working for us. Are you a creative and autonomous Applied Scientist, who loves challenges? Do you have a genuine passion for advancing the state of GPU and CPU across a variety of industries? If so, we want to hear from you.

The base salary range is 148,000 USD - 276,000 USD. Your base salary will be determined based on your location, experience, and the pay of employees in similar positions.

You will also be eligible for equity and benefits (https://www.nvidia.com/en-us/benefits/) . NVIDIA accepts applications on an ongoing basis.

NVIDIA is committed to fostering a diverse work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.

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