About Airwallex
Airwallex is the only unified payments and financial platform for global businesses. Powered by our unique combination of proprietary infrastructure and software, we empower over 100,000 businesses worldwide including Brex, Rippling, Navan, Qantas, SHEIN and many more with fully integrated solutions to manage everything from business accounts, payments, spend management and treasury, to embedded finance at a global scale.
Proudly founded in Melbourne, we have a team of over 1,500 of the brightest and most innovative people in tech located across more than 20 offices across the globe. Valued at US$
- 6 billion and backed by world-leading investors including Sequoia, Lone Pine, Greenoaks, DST Global, Salesforce Ventures and Mastercard, Airwallex is leading the charge in building the global payments and financial platform of the future. If you're ready to do the most ambitious work of your career, join us.
About the team
The risk platform team at Airwallex is responsible for managing the risk for all the products at Airwallex, including GTPN, PA, Issuing, Onboarding, and Account takeover. The risk landscape is constantly changing, and fraudsters are becoming increasingly sophisticated. We are at the forefront of innovation in risk management.
Our mission is to keep Airwallex's products and services safe and secure, and make Airwallex a trusted partner for businesses around the world. We use cutting-edge technologies, such as graph, ML, and LLM, to implement and improve our strategy.
Our team expands across Beijing, Shanghai and Singapore. We collaborate with other teams and our customers globally to ensure a holistic approach for risk management.
What youll do
As a ML engineer, you will work with a team of ML engineers on interesting ML problems in the 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 and graph model; tune model performance; and build applications on top of models to achieve business values.
This role is based in Singapore.
Responsibilities:
- Analyze business requirements and turn them into ML problems
- Work on feature engineering and model training
- Collaborate with different teams to apply models to business problems
- Be an expert about the cutting-edge ML technologies and be able to pick the right model architecture for our problems
- Coach junior ML engineers to grow and be more effective in their job
Who you are
We're looking for people who meet the minimum qualifications for this role. The preferred qualifications are great to have, but are not mandatory.
Minimum qualifications:
- Modeling background in risk domain, fraud detection experience is a plus
- Proficient in Python and/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
- Familiar with SQL
Preferred qualifications:
Desired Majors: CS, Engineering, Math, Physics
Equal opportunity
Airwallex is proud to be an equal opportunity employer. We value diversity and anyone seeking employment at Airwallex is considered based on merit, qualifications, competence and talent. We dont regard color, religion, race, national origin, sexual orientation, ancestry, citizenship, sex, marital or family status, disability, gender, or any other legally protected status when making our hiring decisions. If you have a disability or special need that requires accommodation, please let us know.
Airwallex does not accept unsolicited resumes from search firms/recruiters. Airwallex will not pay any fees to search firms/recruiters if a candidate is submitted by a search firm/recruiter unless an agreement has been entered into with respect to specific open position(s). Search firms/recruiters submitting resumes to Airwallex on an unsolicited basis shall be deemed to accept this condition, regardless of any other provision to the contrary.
