ML Research Engineer Internship, Post-Training - EMEA Remote

As an ML Research Engineer Intern at Hugging Face, you'll focus on post-training research aimed at enhancing the performance of large language models. This involves developing state-of-the-art models, working with evidence-based recipes, and collaborating with talented experts in the field. You'll implement cutting-edge research and help make it accessible through code and datasets.

Candidates for the ML Research Engineer Internship at Hugging Face should possess a strong foundation in machine learning, solid programming skills (particularly in Python), and familiarity with deep learning frameworks. A keen interest in open-source technologies and a collaborative mindset are also essential to thrive in this role.

Hugging Face boasts a supportive, diverse, and inclusive remote working environment. As an intern, you'll have the flexibility to work from anywhere in EMEA while benefiting from collaborative tools and platforms that enable teamwork. You'll also receive support for your workstation setup to help you succeed.

Absolutely! At Hugging Face, we believe in the power of mentorship and collaboration. As an ML Research Engineer Intern, you'll have the opportunity to work closely with industry experts and experienced professionals who are eager to share their insights and knowledge.

Hugging Face is committed to building a diverse team that values different perspectives and backgrounds. Our inclusive culture encourages collaboration and supports every employee regardless of their identity. We aim for a workplace where everyone feels respected and valued, which is vital for fostering innovation.

As an ML Research Engineer Intern, you'll work on exciting projects related to post-training techniques for large language models. This includes implementing strategies to improve model reasoning and enabling models to navigate complex environments, all while contributing to open-source initiatives that impact the AI community.

While the primary goal of the internship is to provide valuable experience, there is always the potential for full-time opportunities at Hugging Face based on performance, contributions, and available positions after the internship concludes.

In your response, highlight your specific role in the project, the techniques you employed, and the outcomes. Discuss the challenges you faced and how you overcame them, showcasing your problem-solving skills and your ability to work within a team.

Share your relevant experiences with LLMs, including any projects where you trained or fine-tuned models. Discuss the frameworks and tools you used, and your approach to data collection, preprocessing, and model evaluation.

Discuss your strategies for staying informed, such as following key researchers on social media, reading academic papers, participating in webinars, or joining professional communities. This shows your commitment to continuous learning in this fast-evolving field.

Talk about specific techniques such as fine-tuning, transfer learning, or using pre-trained models. Discuss any metrics you prioritize during training and how those techniques influence model performance.

Outline a structured approach, including checking data quality, reviewing training logs, validating assumptions in your model architecture, and employing techniques such as ablation studies to identify problematic areas.

Express your passion for Hugging Face's mission to democratize AI and mention specific projects or values that resonate with you, such as open-source contributions or collaborative community efforts.

Provide an example that demonstrates your collaboration skills. Discuss your role within the team, how you communicated effectively, and how you contributed to achieving team goals.

Share your understanding of post-training methods and their importance for enhancing model capabilities, particularly in the context of LLMs. Highlight any specific techniques or frameworks you are familiar with.

Discuss your awareness of the ethical considerations in AI, how you evaluate bias in models, and the measures you'll take to mitigate any potential risks in your training data and deployment.

Share your interests and passions within machine learning, whether it's model optimization, interpretability, or specific applications. Connect these interests back to how they would contribute positively to your role at Hugging Face.