Machine Learning Engineer Intern - Real Production ML

AppLovin; Singapore; On-site; SGD 20,000 - 33,000; Full time

Job summary

AppLovin is seeking Machine Learning Engineer Interns to work on real ML systems, contributing to production pipelines across content recommendation, ranking, user modelling, and content quality. You'll be embedded in a specific ML workstream with a senior engineer mentor and a project scoped for real production impact.

You'll collaborate with product managers and engineers, framing problems, engineering features, training models, and evaluating results with offline and online experiments.

Qualifications

• 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, evaluation, optimisation.
• Proficiency in Python and familiarity with PyTorch or TensorFlow.
• Comfortable working with data — feature engineering and exploration.
• Strong software engineering fundamentals and collaboration.
• Curious and data-honest in interpreting results.

Responsibilities

• Contribute to development and evaluation of ML models within a defined area (recommendation, ranking, content understanding, user modelling).
• Own a focused internship project end-to-end: framing, feature engineering, training, offline evaluation, and online experiments where possible.
• Run experiments and analyse results to form a data-backed view of what works.
• 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

ML concepts

Data analysis

Software engineering fundamentals

Team collaboration

English communication

Education

Bachelor's or Master's in CS/Statistics/Mathematics

Tools

PyTorch

TensorFlow

Spark

Job description

AppLovin is seeking Machine Learning Engineer Interns to work on real ML systems, contributing to production pipelines across content recommendation, ranking, user modelling, and content quality. You'll be embedded in a specific ML workstream with a senior engineer mentor and a project scoped for real production impact.

You'll collaborate with product managers and engineers, framing problems, engineering features, training models, and evaluating results with offline and online experiments.