Internship

Machine Learning Engineer Internship

Hardware Optimization

Confirmed live in the last 24 hours

Hugging Face

Hugging Face

201-500 employees

Develops advanced AI and NLP models

Enterprise Software
AI & Machine Learning

Remote in USA

Category
Applied Machine Learning
AI Research
AI & Machine Learning
Required Skills
Machine Learning

You match the following Hugging Face's candidate preferences

Employers are more likely to interview you if you match these preferences:

Degree
Experience
Requirements
  • Experience with machine learning frameworks, particularly Hugging Face libraries (like Transformers and Diffusers)
  • Familiarity with hardware platforms (AWS Inferentia, AMD CPUs, Nvidia GPUs, etc.)
  • Ability to develop user-friendly tools and guides
  • Strong communication skills for advocating and sharing insights with the community
  • Experience in conducting experiments and analyzing performance across different hardware backends
Responsibilities
  • Develop an online exporter tool to convert Hugging Face models for specific hardware platforms
  • Author comprehensive guides to help users deploy Hugging Face models on various hardware platforms
  • Develop a seamless user flow to guide users from traditional Hugging Face libraries to alternative hardware backends
  • Conduct inference experiments across different hardware backends to identify strengths and weaknesses
  • Collaborate with the Hugging Face Advocacy team to share findings and insights through various channels

Hugging Face develops machine learning models focused on understanding and generating human-like text. Their main products include advanced natural language processing (NLP) models like GPT-2 and XLNet, which can perform tasks such as text completion, translation, and summarization. Users can access these models through a web application and a repository, making it easy to integrate AI into various applications. Unlike many competitors, Hugging Face offers a freemium model, allowing users to access basic features for free while providing subscription plans for advanced functionalities. The company also tailors solutions for large organizations, including custom model training. Hugging Face aims to empower researchers, developers, and enterprises to utilize machine learning for text-related tasks.

Company Stage

Series D

Total Funding

$384.9M

Headquarters

New York City, New York

Founded

2016

Growth & Insights
Headcount

6 month growth

1%

1 year growth

2%

2 year growth

0%
Simplify Jobs

Simplify's Take

What believers are saying

  • Integration of reasoning models could enhance Hugging Face's NLP capabilities.
  • Leveraging ProVision framework may improve Hugging Face's multimodal AI models.
  • Adopting rStar-Math could enhance mathematical reasoning in Hugging Face's models.

What critics are saying

  • MiniMax's LLM with 4M token context challenges Hugging Face's large context handling.
  • Google's Gemini AI may outpace Hugging Face in multi-modal AI capabilities.
  • Novasky's free AI model could undercut Hugging Face's pricing model.

What makes Hugging Face unique

  • Hugging Face offers state-of-the-art NLP models like GPT-2 and XLNet.
  • The company provides a freemium model with advanced features available via subscription.
  • Hugging Face collaborates with tech companies and academic institutions for revenue.

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Benefits

Flexible Work Environment

Health Insurance

Unlimited PTO

Equity

Growth, Training, & Conferences

Generous Parental Leave