ML Engineer Internship - Evaluate

United States - Remote

Hugging Face

We’re on a journey to advance and democratize artificial intelligence through open source and open science.

View company page

Here at Hugging Face, we’re on a journey to advance good Machine Learning and make it more accessible. Along the way, we contribute to the development of technology for the better.

We have built the fastest-growing, open-source library of pre-trained models in the world. With over 100M+ installs and 65K+ stars on GitHub, over 10 thousand companies are using HF technology in production, including leading AI organizations such as Google, Elastic, Salesforce, Algolia, and Grammarly.

About the Role

As an intern on the open-source team, you will work to improve the open-source machine learning ecosystem. You will mainly work on Evaluate, an open-source library to establish better best practices in machine learning and to make model evaluation more accessible and reproducible for engineers and researchers. As part of the internship, you will work on making the library more efficient, extending it to new modalities, and improving its distribution and documentation. You will interact with users and contributors of the broad open-source machine learning ecosystem. We'll brainstorm with you to put you in a position to do the work that interests you and that is impactful.

You'll get to foster one of the most active machine learning communities, helping users contribute to and use the tools that you build. You'll interact with researchers, ML practitioners and data scientists on a daily basis through GitHub, our forums, or Slack.

About you

If you love open-source, are passionate about making complex technology more accessible, and want to contribute to one of the fastest-growing ML ecosystems, then we can't wait to see your application!

If you're interested in joining us, but don't tick every box above, we still encourage you to apply! We're building a diverse team whose skills, experiences, and background complement one another. We're happy to consider where you might be able to make the biggest impact.

Preferred Location

Fully remote.

More about Hugging Face

We are actively working to build a culture that values diversity, equity, and inclusivity. We are intentionally building a workplace where people feel respected and supported—regardless of who you are or where you come from. We believe this is foundational to building a great company and community. Hugging Face is an equal opportunity employer and we do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status.

We value development. You will work with some of the smartest people in our industry. We are an organization that has a bias for impact and is always challenging ourselves to continuously grow. We provide all employees with reimbursement for relevant conferences, training, and education.

We care about your well-being. We offer flexible working hours and remote options. We support our employees wherever they are. While we have office spaces around the world, especially in the US, Canada, and Europe, we're very distributed and all remote employees have the opportunity to visit our offices. If needed, we'll also outfit your workstation to ensure you succeed.

We support the community. We believe significant scientific advancements are the result of collaboration across the field. Join a community supporting the ML/AI community.

* Salary range is an estimate based on our AI, ML, Data Science Salary Index 💰

Tags: GitHub Machine Learning

Perks/benefits: Conferences Flex hours Team events

Regions: Remote/Anywhere North America
Country: United States
Job stats:  63  20  0

More jobs like this

Explore more AI, ML, Data Science career opportunities

Find even more open roles in Artificial Intelligence (AI), Machine Learning (ML), Natural Language Processing (NLP), Computer Vision (CV), Data Engineering, Data Analytics, Big Data, and Data Science in general - ordered by popularity of job title or skills, toolset and products used - below.