Machine Learning Engineering Intern (2027 Summer Internship)
About this role
Curious students who want to build real machine learning systems rather than work with toy datasets will find a great fit in this internship. AppLovin brings together intelligent recommendation, personalisation, and content understanding to power a new social media platform called Gist, operating at a massive scale.
Interns join a cross-functional machine learning team that owns the intelligence layer of the application, handling recommendation engines, ranking algorithms, and user modelling. Working alongside a senior engineer mentor, participants take ownership of a scoped project from problem framing and feature engineering to model training and offline evaluation. The role exists to support the continuous development of production pipelines while giving emerging engineers hands-on experience in a fast-paced ad tech environment.
What you will do
- Contribute to the development and evaluation of machine learning models within defined areas such as recommendation, ranking, content understanding, or user behaviour modelling.
- Own a focused internship project end-to-end, covering problem framing, feature engineering, model training, offline evaluation, and online experimentation where possible.
- Run experiments and analyse results to form a clear, data-backed view of model performance.
- Collaborate closely with engineers and product managers to understand system constraints and translate model improvements into production-ready solutions.
- Participate actively in team discussions regarding model architecture, system design, and metric trade-offs.
What you need
- Currently pursuing a Bachelor's or Master's degree in Computer Science, Statistics, Mathematics, or a related field.
- Demonstrate a solid foundation in machine learning concepts including supervised learning, model evaluation, optimisation, and modelling trade-offs.
- Show proficiency in Python alongside familiarity with at least one machine learning framework like PyTorch or TensorFlow.
- Feel comfortable working with data through feature engineering, exploratory analysis, and experimental interpretation.
- Possess strong software engineering fundamentals covering clean code, version control, and collaboration on shared codebases.
Nice to have
- Project or coursework experience applying machine learning to real data through Kaggle, research, or course capstones
- Exposure to recommendation systems, ranking, or personalisation concepts from academic or independent study
- Familiarity with large-scale data tools such as Spark, Hive, or cloud machine learning platforms
- Understanding of A/B testing and statistical significance in experimentation
- Interest in content platforms, social products, or AI-driven discovery systems
Skills and tech stack
Questions candidates ask
What will I actually work on during the internship?
You will contribute to production pipelines across areas like content recommendation, ranking, user modelling, and content quality depending on your placement. Interns own a focused project end-to-end, ranging from problem framing and feature engineering to model training and offline evaluation.
Who will I be working with on the team?
You will be embedded in a specific machine learning workstream with a senior engineer mentor. The broader team is highly cross-functional, requiring collaboration with product managers, backend engineers, and data analysts.
What qualifications do I need to apply?
Candidates must be currently pursuing a Bachelor's or Master's degree in Computer Science, Statistics, Mathematics, or a related field. You also need a solid foundation in machine learning concepts, proficiency in Python, and familiarity with frameworks like PyTorch or TensorFlow.