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About the role
- Senior Applied Research Scientist developing efficient models for multimodal retrieval. Joining NVIDIA's Retriever team to advance the state of retrieval systems across various modalities.
Responsibilities
- Develop efficient and performant models and pipelines that extract text content from images, video, audio, and other modalities
- Explore and craft datasets, metrics, experiments, and validation scripts to develop standard methodologies for research
- Help ML Engineers scale pipelines to production capability through the development of NVIDIA Inference Microservices (NIMs)
- Write papers, blog posts, documentation, and trainings that help customers understand and take advantage of our research
- Keep up to date with the latest developments in Retrieval across academia and industry.
Requirements
- Master's, Ph.D. or equivalent experience in retrieval or multimodal research preferred
- Track record of publication in leading conferences like SIGIR, KDD, UMAP, RecSys
- Understanding of the state of the art in retrieval research with a focus on multimodal content retrieval
- 5+ 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)
- Ability to communicate ideas clearly through blog posts, papers, kernels, GitHub
- Excellent communication and interpersonal skills
- Ability to work in a dynamic, product-oriented, distributed team
- History of mentoring junior engineers and interns is a plus.
Benefits
- Competitive salary package
- Benefits
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