Hugging Face invites candidates to apply for various roles, aiming to match them with opportunities that leverage their talents and promote a diverse workplace culture.

The Senior Node.js Software Engineer at Hugging Face will develop tooling for the Machine Learning Hub, collaborating across the stack and focusing on user-centric products.

As a Machine Learning Research Engineer Intern at Hugging Face, you'll contribute to building high-quality web datasets and data processing libraries while collaborating with a diverse team focused on open-source solutions.

The Technical Solution Engineer will support customers in building integrations with Hugging Face Hub, maintain documentation, and assist the support team with technical issues.

As an ML Research Engineer Intern, you'll build high-quality web data and contribute to running distributed data processing while enhancing data quality with small models.

The Machine Learning Engineer Intern at Hugging Face is responsible for expanding tools that help users understand and write Gradio code, leveraging Gradio spaces and existing user prompts to build sophisticated model pipelines. The intern will experiment with modern deep learning libraries and LLM APIs to improve user experience and contribute to a growing community of developers.

The internship focuses on expanding Hugging Face's ecosystem for web developers by creating and maintaining JavaScript/TypeScript machine learning libraries, optimizing models for in-browser inference, and building demo applications to showcase new features.

The Cloud Machine Learning Engineer at Hugging Face will develop machine learning solutions for cloud platforms, integrate Hugging Face libraries, build user-friendly APIs, and document features. They require experience with machine learning, deep learning frameworks, cloud services, and MLOps pipelines.

As a Machine Learning Engineer in the Optimization team at Hugging Face, you will integrate hardware and AI models, ensuring their performance. You will design Python APIs focused on performance and efficiency in deploying ML models, while collaborating closely with hardware partners.

As a Machine Learning Optimization Evangelist, you will enhance Hugging Face's ML Optimization team by educating the ML community on efficient model training and inference. Responsibilities include publishing technical content, contributing to documentation, public speaking, and collaborating on product strategies with AI Accelerators and cloud platforms.

As a Machine Learning Engineer at Hugging Face, you will develop specialized software for machine learning applications, enhance software quality, manage production environments, and optimize system performance collaborating with a talented team in an inclusive culture.

Manage IT hardware and support users at the office by providing technical assistance, handling procurement for computer accessories, and ensuring the smooth operation of office technology and infrastructure.

As an Open-Source Machine Learning Engineer at Hugging Face, you will enhance the machine learning ecosystem by improving open-source libraries and collaborating with researchers and data scientists. Your role includes working with community contributors and fostering dynamic discussions about tool usage and development.

As a Machine Learning Engineer at Hugging Face, you will develop specialized software focused on real-world ML use cases, enhance system performance, create scalable solutions, and manage production environments. You should bring innovative ideas and contribute to building AI technologies within a collaborative team.

The Cloud Machine Learning Engineer at Hugging Face will build machine learning solutions leveraging cloud technologies, focusing on integrating Hugging Face libraries with cloud platforms and enhancing developer experiences through secure APIs. Responsibilities include ensuring model performance, writing documentation, and advocating work within the community.

The Machine Learning Engineer Intern at Hugging Face will expand tools for users to understand and write Gradio code. They will engage with Gradio's vast library and assist in building sophisticated model pipelines, ensuring context for developers using LLMs.

The role involves expanding Hugging Face's ecosystem for web developers by creating and maintaining machine learning libraries in JavaScript and TypeScript, optimizing models for browser inference, and building demo applications. It combines software engineering with machine learning and fosters open-source collaboration.

As a Machine Learning Optimization Evangelist, you will educate the ML community on optimizing their training and inference workloads. Your role includes publishing technical blog posts, contributing to documentation, speaking at conferences, producing webinars, and collaborating with strategic partners to enhance the usage of Hugging Face models on various compute platforms.

As a Senior Frontend Engineer, you will develop complex UI components using JavaScript and Python, focusing on making machine learning accessible. Collaborating with researchers and developers, you'll maintain an open-source library, responding to the evolving needs of users.

The Machine Learning Engineer will focus on integrating Hugging Face models with AI hardware, ensure model performance, create user-friendly Python APIs, and deploy models efficiently.