[Campus Recruitment] Temasek Associate Internship Programme 2022 - Digital Tech, ML (Singapore)


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Job Summary

Job Type


Years of Experience
Information not provided

Tech Stacks

Job Description

During this minimum 12-week summer programme, your focus will be in one of the areas reflected below and at the same time, you will be fully immersed in the Temasek culture.

Our comprehensive training programmes will help you develop highly relevant skills and knowledge that you will be able to apply throughout your internship. We believe in providing ongoing performance feedback and evaluations to facilitate your continuous development, and we aim for this to culminate in your conversion to a full-time Associate with us.

We are looking for undergraduate students graduating in Dec 2022 to Mid-2023.

Full-time interns preferred and please let us know in the event that you are available for an extended internship period of 3-6 months.

Machine Learning Engineering Intern

We are looking for Software Engineers with an interest and experience in developing practical applications powered by Machine Learning. Partnering closely with other team members in an agile setup, the candidate will develop software and tooling for digital initiatives, leveraging big data, machine learning and cloud technologies.

  • Develop machine learning models to solve practical challenges, working mostly (but not exclusively) with text / language data.
  • Apply principled ways of evaluating and characterizing the expected run-time performance of ML models.
  • Develop tooling and software for integrating ML models with web / mobile applications.


Minimum Requirement

  • Development knowledge and experience in Python
  • Strong communication and interpersonal skills.
  • Good grasp of key computer science and software engineering concepts including but not limited to software design patterns, application security, performance, unit testing etc.

Preferred Requirement
  • Preferred programs include: Engineering, Computer Science, Computer Engineering.
  • Experience applying machine learning to solve practical problems.
  • Strong foundations in probability, statistics and linear algebra.
  • Experience with deep learning and / or bayesian methods (MCMC).
  • Experience with the ecosystem of common ML tools: TensorFlow, PyTorch, Numpy / Scipy / Scikit-learn, Pandas, etc.


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