Machine Learning Engineer Intern

Summary

Join Hugging Face, a leading platform for AI builders, as a Machine Learning Engineering Intern. You will contribute to the expansion of Gradio, a widely used library for building and sharing machine learning demos. The internship focuses on enhancing Gradio's AI tools, particularly the Playground code editor, by leveraging existing data to build sophisticated model pipelines. You will work with LLMs to improve code generation and address the lack of Gradio-specific context in existing models. This role requires experience with deep learning libraries, LLMs, and fine-tuning techniques. Hugging Face offers a supportive and inclusive work environment with flexible working hours, remote options, and professional development opportunities.

Requirements

• Experience using modern deep learning libraries and LLM APIs and understanding tradeoffs between different models and APIs
• Knowledge of how to fine-tune LLMs, create retrieval-augmented generation pipelines, and use re-rankers for two-stage retrieval
• Comfort exploring and contributing to a codebase consisting of Python and Svelte-flavored JavaScript
• A "product mindset" -- experimenting with different user interfaces to build products that are useful to hundreds of thousands of users

Responsibilities

• Expand the effort to enhance Gradio's AI tools, particularly the Playground code editor
• Build a sophisticated model pipeline with the proper context of how Gradio works, leveraging ~500k Gradio spaces and thousands of existing prompts from users
• Improve LLMs to better understand and generate Gradio code

Preferred Qualifications

• Passion for open source and making advanced ML tools accessible
• Staying up to date with the latest machine learning trends and models
• A high bias for action and results

Benefits

• Reimbursement for relevant conferences, training, and education
• Flexible working hours and remote options
• Support for employees regardless of location; opportunity to visit offices worldwide
• Workstation outfitting if needed