Machine Learning Engineering Intern (2027 Summer Internship)

Location: Singapore

Department: Platform Engineering

At AppLovin, AI isn't a feature — it's the foundation. We are building a next-generation global content platform that uses intelligent recommendation, personalisation, and content understanding to help people discover experiences that genuinely matter to them. Backed by AppLovin's world-class AI capabilities and global infrastructure, our ML team is working on some of the most technically interesting problems in the recommendation and content space — at a scale few startups ever reach.
For Summer 2027, we're looking for Machine Learning Engineer Interns who want to work on real ML systems, not toy datasets. You'll contribute to production pipelines across areas like content recommendation, ranking, user modelling, and content quality — depending on where you're placed and where you can make the most impact.

About Gist
Gist is a new social media platform for people who want substance — practical, thoughtful content that spans philosophy debates, city-specific recommendations, seasonal gift guides, and wellness tips. Across categories like travel, food, career, and family, we describe it as "a handbook for the curious."
Gist is built by AppLovin Corporation — a company that hit $5.3B in revenue last year and sits alongside Google and Meta as one of the world's leading ad tech platforms. Fortune recognizes AppLovin as one of the Best Workplaces in the Bay Area, and the company has been a Certified Great Place to Work for the last four years (2021–2024).

About the Team
Our ML team owns the intelligence layer of the platform — the systems that decide what content a user sees, how it's ranked, how new users and new content are onboarded, and how we balance engagement, retention, and ecosystem health over time. It's a highly cross-functional team: you'll work alongside product managers, backend engineers, and data analysts to turn model improvements into measurable business outcomes. As an intern, you'll be embedded in a specific ML workstream with a senior engineer mentor and a project scoped for real production impact.

What You'll Do

• Contribute to the development and evaluation of ML models within a defined area — recommendation, ranking, content understanding, or user behaviour modelling.
• Own a focused internship project end-to-end: problem framing, feature engineering, model training, offline evaluation, and where possible, online experimentation.
• Run experiments and analyse results to form a clear, data-backed view of what's working and why.
• Collaborate with engineers and product managers to understand system constraints and translate model improvements into production-ready solutions.
• Participate in team discussions on model architecture, system design, and metric trade-offs.

Requirements

• Currently pursuing a Bachelor's or Master's degree in Computer Science, Statistics, Mathematics, or a related field
• Solid foundation in machine learning concepts — supervised learning, model evaluation, optimisation, and the trade-offs between different modelling approaches.
• Proficiency in Python and familiarity with at least one ML framework such as PyTorch or TensorFlow.
• Comfortable working with data — feature engineering, exploratory analysis, and interpreting experimental results.
• Strong software engineering fundamentals: clean code, version control, and the ability to collaborate on a shared codebase.
• Curious, rigorous, and honest about what the data is actually telling you.

Good To Have

• Any project or coursework experience applying ML to real data — Kaggle competitions, research projects, and course capstones all count.
• Exposure to recommendation systems, ranking, or personalisation concepts — even from an academic or reading background.
• Familiarity with large-scale data tools such as Spark, Hive, or cloud ML platforms.
• Understanding of A/B testing and statistical significance in an experimentation context.
• Interest in content platforms, social products, or AI-driven discovery systems.