AppLovin - Machine Learning Engineering Intern (2027 Summer Internship)

Singapore3w ago

In Office Intern APAC Artificial Intelligence Nonprofit Engineering Intern Software Engineering Intern Python Kaggle

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.

Responsibilities

• 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.