Senior Applied Research Scientist, Multimodal Retrieval at NVIDIA

Santa Clara, California, United States

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Not SpecifiedCompensation

Senior (5 to 8 years)Experience Level

Full TimeJob Type

UnknownVisa

Technology, AI, Machine LearningIndustries

Requirements

Master's, Ph.D., or equivalent experience in retrieval or multimodal research, with a track record of publications in leading conferences like SIGIR, KDD, UMAP, RecSys, etc
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 strong understanding of the Python deep learning ecosystem (PyTorch, Tensorflow, MXNet, etc.)
Ability to share and communicate ideas clearly through blog posts, papers, kernels, GitHub, etc
Excellent communication and interpersonal skills, with ability to work in a dynamic, product-oriented, distributed team
History of mentoring junior engineers and interns is a plus

Responsibilities

Work with team of researchers to 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, offering customers clear guidance on models and pipelines for specific contexts
Help ML Engineers scale pipelines to production capability through development of NVIDIA Inference Microservices (NIMs) and blueprints demonstrating effective deployment in pipelines
Write papers, blog posts, documentation, and trainings to help customers understand and utilize the research
Keep up to date with the latest developments in Retrieval across academia and industry

Skills

Key technologies and capabilities for this role

Deep LearningMultimodal RetrievalRAGText EmbeddingsNVIDIA Inference MicroservicesNIMsMLOpsDataset CurationModel DeploymentRetrieval ResearchSIGIRKDD

Questions & Answers

Common questions about this position

What salary or compensation can I expect for this role?

NVIDIA offers a competitive salary package and benefits.

Is this position remote or does it require office work?

This information is not specified in the job description.

What key skills are required for this Senior Applied Research Scientist role?

Candidates need 5+ years of experience developing multimodal systems, excellent Python programming skills with deep learning frameworks like PyTorch, and knowledge of best practices in batching, streaming, and scaling ingestion pipelines. A strong background in retrieval research, especially multimodal, and a track record of publications in conferences like SIGIR or KDD are preferred.

What is the team culture like at NVIDIA's Retriever team?

The team consists of Applied Research Scientists, Machine Learning and MLOps Engineers in a dynamic, product-oriented, distributed environment with forward-thinking and hardworking people. Excellent communication, interpersonal skills, and a history of mentoring junior engineers are valued.

What makes a strong candidate for this position?

A strong candidate has a Master's or Ph.D. in retrieval or multimodal research with publications in top conferences, 5+ years in multimodal systems, Python and deep learning expertise, and the ability to communicate ideas through papers, blogs, and GitHub while thriving in a distributed team.

NVIDIA

Designs GPUs and AI computing solutions

About NVIDIA

NVIDIA designs and manufactures graphics processing units (GPUs) and system on a chip units (SoCs) for various markets, including gaming, professional visualization, data centers, and automotive. Their products include GPUs tailored for gaming and professional use, as well as platforms for artificial intelligence (AI) and high-performance computing (HPC) that cater to developers, data scientists, and IT administrators. NVIDIA generates revenue through the sale of hardware, software solutions, and cloud-based services, such as NVIDIA CloudXR and NGC, which enhance experiences in AI, machine learning, and computer vision. What sets NVIDIA apart from competitors is its strong focus on research and development, allowing it to maintain a leadership position in a competitive market. The company's goal is to drive innovation and provide advanced solutions that meet the needs of a diverse clientele, including gamers, researchers, and enterprises.

Santa Clara, CaliforniaHeadquarters

1993Year Founded

$19.5MTotal Funding

IPOCompany Stage

Automotive & Transportation, Enterprise Software, AI & Machine Learning, GamingIndustries

10,001+Employees

Benefits

Company Equity

401(k) Company Match

Risks

Increased competition from AI startups like xAI could challenge NVIDIA's market position.

Serve Robotics' expansion may divert resources from NVIDIA's core GPU and AI businesses.

Integration of VinBrain may pose challenges and distract from NVIDIA's primary operations.

Differentiation

NVIDIA leads in AI and HPC solutions with cutting-edge GPU technology.

The company excels in diverse markets, including gaming, data centers, and autonomous vehicles.

NVIDIA's cloud services, like CloudXR, offer scalable solutions for AI and machine learning.

Upsides

Acquisition of VinBrain enhances NVIDIA's AI capabilities in the healthcare sector.

Investment in Nebius Group boosts NVIDIA's AI infrastructure and cloud platform offerings.

Serve Robotics' expansion, backed by NVIDIA, highlights growth in autonomous delivery services.

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