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Machine Learning Engineer Internship, Generative AI - US Remote

At Hugging Face, we’re on a journey to democratize good AI. We are building the fastest growing platform for AI builders with over 5 million users & 100k organizations who collectively shared over 1M models, 300k datasets & 300k apps. Our open-source libraries have more than 400k+ stars on Github.

About the Role

ChatGPT took the world by storm due to its surprising capabilities. By the time it was launched it was a massive model, requiring many GPUs to run. Fast forward to 2024, and we have 1B LLMs that fit in a smartphone and have similar quality levels. The community has also realized that small models using techniques like chain of thought can outperform much larger models. The focus of this internship is on model usage: how can we extract a higher-quality output from existing small-sized pre-trained models?

This internship works at the intersection of software engineering and machine learning engineering, bringing state-of-the-art generative breakthroughs to transformers in a user-friendly fashion. By the end of this internship, the candidate will have touched all facets that power LLM APIs, including hardware acceleration, numerical precision problems, common machine learning caveats, and the importance of writing scalable software.

About You

If you love open-source but also have an eye for art and creativity, are passionate about making complex technology more accessible to engineers and artists, 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.

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.

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What You Should Know About Machine Learning Engineer Internship, Generative AI - US Remote, Hugging Face

Welcome to Hugging Face, where innovation meets inclusion in the exciting realm of AI! We're on the lookout for a passionate Machine Learning Engineer Intern to join our Generative AI team, and we want you to be a part of this transformative journey. As a Machine Learning Engineer Intern, you will delve deep into the world of large language models (LLMs), exploring how even the tiniest of models can yield incredible results. Imagine collaborating with a vibrant team of AI enthusiasts to enhance the usability of pre-trained models, making them more accessible and effective for engineers and artists alike. This internship will equip you with hands-on experience in tackling complex challenges around numerical precision, hardware acceleration, and scalable software development. You'll engage with some of the brightest minds in the industry, contributing to cutting-edge projects that push the boundaries of what's possible in machine learning. Plus, we believe in nurturing creativity and diversity—if you're ready to share your unique ideas and perspectives, we're excited to see how you can shape our future. At Hugging Face, you will receive the support you need to thrive in a flexible work environment that values work-life balance and innovation. Whether you work from home or visit our global offices, you'll have the tools and resources to succeed. So, if you're ready to kickstart your career in machine learning and be a part of a rapidly growing ML ecosystem, we’d love to hear from you!

Frequently Asked Questions (FAQs) for Machine Learning Engineer Internship, Generative AI - US Remote Role at Hugging Face
What does a Machine Learning Engineer Internship at Hugging Face involve?

The Machine Learning Engineer Internship at Hugging Face focuses on enhancing the usability of small-sized pre-trained models, improving model outputs, and understanding the intricacies of software engineering and machine learning. Interns work on real-world applications, involving hardware acceleration and writing scalable software. It's an incredible opportunity to dive into the world of generative AI and make a significant impact.

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What skills are required for the Machine Learning Engineer Internship at Hugging Face?

Candidates for the Machine Learning Engineer Internship at Hugging Face should ideally have a background in machine learning, software engineering, and an understanding of generative models. Familiarity with coding, problem-solving skills, and an enthusiasm for open-source projects are also essential. However, Hugging Face welcomes applicants from diverse backgrounds, so if you're passionate about ML, you should apply!

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What is the work culture like at Hugging Face for Machine Learning Engineer Interns?

Hugging Face fosters a culture of inclusivity, flexibility, and respect. As a Machine Learning Engineer Intern, you'll work in a supportive environment that values diversity and collaboration. The team values continuous growth and encourages innovative contributions, equipping you with the tools needed for both personal and professional development.

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Is the Machine Learning Engineer Internship at Hugging Face remote?

Yes, the Machine Learning Engineer Internship at Hugging Face is designed to be remote-first, allowing flexibility in your work location. While Hugging Face has offices around the world, you'll have the opportunity to work from home and visit any of our locations if needed. This helps maintain a healthy work-life balance while still being part of a dynamic team.

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What kind of projects can a Machine Learning Engineer Intern expect to work on at Hugging Face?

Interns at Hugging Face will engage in projects that enhance the performance of existing LLM APIs, focusing on extracting high-quality outputs from small-sized models. This includes tackling computational challenges and exploring innovative generative techniques. You'll gain practical experience that is valuable in the machine learning field.

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What opportunities for learning and development are available for interns at Hugging Face?

Hugging Face prioritizes the professional growth of its interns. As a Machine Learning Engineer Intern, you'll have access to relevant training, conferences, and educational resources. The organization encourages learning through collaborative projects and mentorship from experienced industry professionals, ensuring a rich learning experience.

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How does Hugging Face support diversity in the workplace for Machine Learning Engineer Interns?

Hugging Face actively promotes diversity, equity, and inclusivity within its organization. The company strives to create a respectful environment where all backgrounds are valued, fostering a sense of belonging for every employee, including Machine Learning Engineer Interns. Their commitment to building diverse teams enhances the innovative spirit of the organization.

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Common Interview Questions for Machine Learning Engineer Internship, Generative AI - US Remote
How do you approach working with pre-trained models in machine learning?

Discuss your understanding of pre-trained models, their benefits, and challenges. Highlight any previous experience you have with fine-tuning models for specific tasks. It's also valuable to mention your familiarity with frameworks like TensorFlow or PyTorch, showcasing your technical skills in handling these models effectively.

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Can you explain the value of hardware acceleration in machine learning?

Provide an explanation of hardware acceleration and its role in speeding up computations in machine learning tasks. Discuss various types of hardware, such as GPUs and TPUs, and how they can drastically improve the performance of training large models. Use examples to demonstrate your understanding.

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What is your favorite ML project you've worked on and why?

Share a project that exemplifies your skills and interests, ideally relevant to the internship. Discuss your role, the challenges faced, and how you overcame them. Highlight the technologies used and the lessons learned, emphasizing your passion for machine learning and its applications.

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How do you address numerical precision problems in your work?

Discuss strategies for handling numerical precision issues, such as adjusting the data type used for calculations or employing techniques like mixed precision training. It's important to show that you understand the implications of numerical precision on model performance and reliability.

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What interests you about generative AI?

Share your insights on generative AI's impact on various industries and your personal interest in the field. Relay any relevant research, projects, or ideas you've explored, demonstrating your enthusiasm for advancing generative AI technology and its potential applications.

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How do you ensure your software is scalable?

Explain best practices for writing scalable software, such as modular design, efficient algorithms, and code performance optimizations. Sharing examples from past experiences helps to provide clarity on your approach and the importance of building scalable solutions in engineering.

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Can you describe a time when you collaborated with a team to solve a machine learning problem?

Use the STAR method (Situation, Task, Action, Result) to narrate your experience in team collaboration. Highlight your role, how you contributed to group dynamics, and the end result, emphasizing the importance of teamwork in solving complex machine learning challenges.

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What is the significance of community involvement in AI and machine learning?

Discuss the role of the community in driving innovation and knowledge sharing within the AI and machine learning fields. Mention how collaboration leads to improved practices, resource sharing, and community-driven projects that benefit the ecosystem, and your engagement in such activities.

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What steps do you take when debugging a machine learning model?

Outline a systematic approach to debugging, including checking data quality, understanding model architecture, interpreting results, and adjusting hyperparameters. Emphasize your analytical mindset and willingness to experiment to resolve issues effectively.

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How do you keep yourself updated with the latest developments in machine learning?

Share how you follow prominent research publications, attend conferences, engage with online communities, or participate in workshops to stay abreast of the latest trends and advancements in machine learning. Show your dedication to continuous learning and improvement.

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Internship, remote
DATE POSTED
November 28, 2024

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