Cloud Machine Learning Engineer - US remote at Hugging Face

United States

Apply Now

Not SpecifiedCompensation

Junior (1 to 2 years)Experience Level

Full TimeJob Type

UnknownVisa

Artificial Intelligence, SoftwareIndustries

Requirements

Candidates should possess deep experience building with Hugging Face Technologies, including Transformers, Diffusers, Accelerate, PEFT, and Datasets, along with expertise in Deep Learning Frameworks, preferably PyTorch or XLA. Strong knowledge of cloud platforms such as AWS, Azure, or GCP, including services like Amazon SageMaker, EC2, S3, CloudWatch, and familiarity with MLOps pipelines, containerization with Docker, and experience with Typescript, Rust, and MongoDB are required.

Responsibilities

The Cloud Machine Learning Engineer will be responsible for bridging and integrating Hugging Face's open-source models like Transformers and Diffusers with various cloud providers, ensuring performance, designing and developing easy-to-use APIs, writing technical documentation, sharing work with the community, and advocating for new features. They will also contribute to building machine learning solutions used by millions, leveraging cloud technologies, and working across the full product development lifecycle.

Skills

Machine Learning

Cloud Technologies

Transformers

Diffusers

API Development

Technical Documentation

Hugging Face

Develops advanced NLP models for text tasks

About Hugging Face

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.

New York City, New YorkHeadquarters

2016Year Founded

$384.9MTotal Funding

SERIES_DCompany Stage

Enterprise Software, AI & Machine LearningIndustries

201-500Employees

Benefits

Flexible Work Environment

Health Insurance

Unlimited PTO

Equity

Growth, Training, & Conferences

Generous Parental Leave

Risks

Salesforce's exclusive partnerships may limit Hugging Face's access to training datasets.

Microsoft's rStar-Math technique could outperform Hugging Face's NLP models in specific tasks.

DeepSeek-V3's release may intensify competition in the ultra-large model space.

Differentiation

Hugging Face offers a massive library of over one million AI models.

The company provides a thriving online community for open-source AI collaboration.

Hugging Face's freemium model allows easy access to advanced NLP tools.

Upsides

Collaboration with Microsoft could enhance small model performance in mathematical reasoning.

Partnership with DeepSeek may boost Hugging Face's ultra-large model capabilities.

Integration with Salesforce's ProVision could improve multimodal AI capabilities.

Land your dream remote job 3x faster with AI

Try Jobo Free