The Opportunity：

Founded in 2015, Airwallex is a global payments fintech transforming the way businesses move and manage money domestically and internationally, headquartered in Hong Kong.

In today’s fast-changing digital era, our purpose is to empower businesses of all sizes to grow in their own markets and around the world, and by doing so, contribute to the global economy. To achieve our mission, we have built a proprietary global financial infrastructure platform that offers extensive coverage across 130+ countries and 50+ currencies. Our platform enables businesses operating globally to transact, collect and pay in any foreign currency without the constraints of the current global financial system.

In just five years since we were founded, Airwallex has grown to become one of Hong Kong’s fintech unicorns and a well-funded international technology leader. We are backed by top-tier investors such as Tencent, Sequoia Capital China, Horizon Ventures, DST Global and Salesforce Ventures.

Airwallexs innovation and scale have been recognised and awarded by leading independent authorities, such listing in Forbes Cloud 100 2020, and placing in the Top 50 of KPMG’s Global Fintech100 two years in a row.

The Role:

As an ML engineer intern, you will work with your mentor on interesting ML problems in risk management domain. You will build sophisticated features and models based on real world transaction data. We expect you to experiment with different features; try out various model architectures, from random forest, xgboost to DNN, LSTM; tune model performance; and build applications on top of models to achieve business values.

Responsibilities：Analyze business requirements and turn them into ML problemsWork on feature engineering and model trainingBuild machine learning platform and tools to make ML easier at AirwallexCollaborate with different teams to apply models to solve business problems

Qualifications： Proficient in Python or a JVM language (Java, Scala, Kotlin)Experience with model training and ML libraries, such as scikit-learn, Tensorflow, PyTorch, Keras, etc.Experience with big data frameworks, such as Flink or Spark is a plusFamiliar with SQL is a plus