Machine Learning Engineer Intern

Summary

Join Hugging Face, a leading platform for AI builders, as an intern to contribute to the TRL library, a crucial tool for post-training of large language models. You will collaborate with the research team, integrating cutting-edge methods, maintaining the codebase, and engaging with the community. This internship offers hands-on experience in fine-tuning LLMs, distributed training, and open-source development. You will directly impact thousands of developers globally, advancing the adoption of state-of-the-art post-training techniques. Hugging Face values diversity and provides a supportive work environment with flexible hours and remote options, along with professional development opportunities.

Requirements

• Knowledge and experience in fine-tuning large language models (LLMs) or vision-language models (VLMs), and optimization techniques
• Proficiency in Python, PyTorch, and frameworks like Hugging Face Transformers
• Experience in distributed training and GPU acceleration
• Familiarity with Git/GitHub workflows, community engagement, documentation, and collaborative development
• Exposure to cutting-edge ML research, benchmarking, and testing fine-tuning methods
• Experience in building tools to streamline workflows, ensuring software stability, backward compatibility, versioning, and delivering reliable releases

Responsibilities

• Integrate cutting-edge methods into the TRL library
• Maintain a clean and scalable codebase
• Ensure usability through thoughtful documentation
• Engage with the TRL community by responding to issues
• Gather feedback and foster collaboration through thoughtful discussions and support
• Ensure the library continues to meet developers' needs

Preferred Qualifications

Experience writing blog posts, tutorials, and sharing updates on platforms like LinkedIn to engage with the community and make complex concepts accessible to a wider audience

Benefits

• Reimbursement for relevant conferences, training, and education
• Flexible working hours and remote options
• Opportunity to visit offices (if remote)
• Workstation outfitting (if needed)