ML Engineer Intern — Build Real Production Models

AppLovin; Singapore; On-site; SGD 16,000 - 22,000; Full time

Job summary

AppLovin is seeking a Machine Learning Engineering Intern for Summer 2027. You will join a cross-functional ML team building production ML pipelines for recommendation, ranking, and content understanding, and will own a scoped project end-to-end from framing to evaluation.

The role emphasizes hands-on work with Python, ML models, and collaboration with PMs and engineers to deliver real production impact.

Qualifications

• Pursuing a Bachelor's or Master's in Computer Science, Statistics, Mathematics, or related field.
• Solid foundation in machine learning concepts (supervised learning, model evaluation, optimization) and trade-offs.
• 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 results.
• Strong software engineering fundamentals: clean code, version control, and collaboration on a shared codebase.

Responsibilities

• Contribute to development and evaluation of ML models in a defined area (e.g., recommendation, ranking, content understanding, or user modeling).
• Own a focused internship project end-to-end: framing problem, feature engineering, model training, offline evaluation, and potential online experiments.
• Run experiments and analyse results to form a data-backed view of what works and why.
• Collaborate with engineers and product managers to translate model improvements into production-ready solutions.
• Participate in team discussions on model architecture, system design, and metric trade-offs.

Skills

Python

PyTorch

TensorFlow

ML concepts

Data analysis

Software fundamentals

Collaboration

Curiosity

Education

Bachelor's or Master's in Computer Science / Statistics / Mathematics

Tools

Spark

Hive

Cloud ML platforms

Job description

AppLovin is seeking a Machine Learning Engineering Intern for Summer 2027. You will join a cross-functional ML team building production ML pipelines for recommendation, ranking, and content understanding, and will own a scoped project end-to-end from framing to evaluation.

The role emphasizes hands-on work with Python, ML models, and collaboration with PMs and engineers to deliver real production impact.