Intern (TGI - Machine Learning/ AI Engineering)

Franklin Templeton · Singapore

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At Franklin Templeton, we believe success is built through powerful partnerships. As a forward thinking asset manager, we build dynamic relationships with clients, understand their goals, and navigate complex markets together. We leverage cutting edge strategies and deep insights to unlock opportunities for long term wealth creation. Our talented, global teams bring expertise that is both broad and unique.

From our welcoming, inclusive, and supportive culture to our globally diverse business, we offer opportunities not only to help you reach your potential, but also to contribute to our clients’ success.

We are looking for an Intern to join the Templeton Global Investments team in Singapore office. The internship is expected to commence in December 2026 for 25 to 35 weeks.

We are seeking a technically strong and curious intern to work on applied machine learning research and AI application development. The intern will explore model optimization techniques, develop reusable machine-learning components, and build practical AI-powered tools for internal use. This role combines experimental data science with hands-on software engineering and is suited to someone who enjoys translating research ideas into reliable working systems.

How will you add value?

• Research automated feature engineering, feature selection, model optimization techniques and optimization approaches.
• Develop reproducible model-training and evaluation workflows, reusable machine-learning pipelines and components.
• Build proof-of-concept Artificial Intelligence tools, applications, backend services, Application Programming Interfaces and lightweight interfaces for internal workflows.
• Track experiments, results and computational performance, and evaluate solutions for accuracy, reliability and practical usefulness.
• Write tested, maintainable and well-documented Python code.
• Present findings, limitations and recommendations to stakeholders, and prepare technical documentation and knowledge-transfer sessions.

What will help you be successful in this role?

• Currently pursuing or recently completed a degree in Computer Science, Artificial Intelligence, Data Science, Statistics, Mathematics, Operations Research, Engineering or a related quantitative discipline.
• Strong Python programming ability with a sound understanding of machine-learning fundamentals.
• Familiarity with feature engineering, model evaluation, hyperparameter optimization and libraries such as NumPy, pandas, scikit-learn and XGBoost.
• Ability to design experiments carefully, interpret results critically and work with Git or an existing codebase.
• Strong problem-solving, communication and documentation skills, with the ability to work independently while seeking guidance when appropriate.
• Knowledge of genetic algorithms, evolutionary computation and optimization methods.
• Experience with time-series modelling, validation and experiment-tracking frameworks.
• Familiarity with Structured Query Language and data pipelines.
• Exposure to Representational State Transfer Application Programming Interfaces, lightweight application development and production-oriented software engineering.
• Interest in large language models, retrieval-augmented generation, agentic workflows, cloud platforms, containers and continuous integration / continuous delivery.

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