Internship

Applied Research PhD Intern

Retriever

Posted on 11/27/2024

NVIDIA

NVIDIA

10,001+ employees

Designs GPUs and AI computing solutions

Automotive & Transportation
Enterprise Software
AI & Machine Learning
Gaming

Remote in UK

Includes remote options for France and Germany.

Category
Applied Machine Learning
Deep Learning
Computer Vision
AI & Machine Learning
Required Skills
Python
Pytorch
Computer Vision
Requirements
  • Pursuing a PhD in Computer Science or other relevant technical fields
  • Excellent Python programming skills and a strong understanding of the Python deep learning ecosystem (in particular PyTorch)
  • Excellent knowledge of the current state of Deep Learning, including experience fine-tuning state of the art Large Language Models and Computer Vision models
  • Excellent communication skills and the ability to share and communicate your ideas clearly through blog posts, papers, kernels, GitHub, etc.
Responsibilities
  • Working with our team of researchers to fine-tune information retrieval models and develop pipelines for text, image, video, audio, and other modalities content.
  • Exploring and crafting datasets, designing metrics, running experiments, and evaluating models in order to develop standard methodologies. These methodologies will offer customers clear guidance on which models and pipelines to apply in specific contexts.
  • Helping ML Engineers bring new Retrieval models to production as NVIDIA Inference Microservices (NIMs) or blueprints
  • Writing blog posts, documentation, training materials and potentially papers, that help customers understand and take advantage of our research
  • Keeping up to date with the latest developments in Retrieval across academia and industry

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, such as GPUs, are essential for high-performance computing and artificial intelligence applications. NVIDIA's GPUs work by processing large amounts of data simultaneously, making them ideal for tasks like gaming graphics and complex computations in AI and machine learning. Unlike many competitors, NVIDIA focuses on both hardware and software solutions, offering cloud-based services that enhance the capabilities of their products. The company's goal is to drive innovation in AI and HPC, providing advanced solutions that cater to a wide range of clients, from gamers to researchers and enterprises.

Company Stage

IPO

Total Funding

$19.5M

Headquarters

Santa Clara, California

Founded

1993

Growth & Insights
Headcount

6 month growth

15%

1 year growth

27%

2 year growth

45%
Simplify Jobs

Simplify's Take

What believers are saying

  • NVIDIA's diverse investment portfolio offers employees exposure to cutting-edge technologies and industries, fostering a dynamic and innovative work environment.
  • The company's strategic acquisitions and partnerships provide opportunities for professional growth and cross-industry collaboration.
  • NVIDIA's strong financial backing and market leadership ensure stability and resources for continued innovation and development.

What critics are saying

  • NVIDIA's expansion into various sectors may dilute its focus and resources, potentially impacting its core GPU business.
  • The competitive landscape in AI, robotics, and autonomous vehicles is intense, posing challenges for NVIDIA to maintain its leadership position.

What makes NVIDIA unique

  • NVIDIA's strategic investments in diverse sectors like robotics, AI disease modeling, and driverless trucks highlight its commitment to pioneering technologies beyond its core GPU business.
  • The acquisition of Shoreline.io, a software startup, indicates NVIDIA's focus on enhancing its software capabilities, setting it apart from competitors primarily focused on hardware.
  • NVIDIA's involvement in significant funding rounds for companies like Mistral AI and Bright Machines showcases its influence and leadership in the AI and intelligent manufacturing sectors.

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