Senior Software Engineer, Machine Learning Platform

Roles & Responsibilities

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 150,000 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 1,700 of the brightest and most innovative people in tech across more than 37 locations around the globe. Valued at US$5.6 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 team builds and maintains the robust, scalable infrastructure that powers our advanced machine learning models, enabling rapid iteration and deployment of risk strategies.

What you’ll do

Our mission is to keep Airwallex’s products and services safe and secure, and make Airwallex a trusted partner for businesses around the world. You will be instrumental in designing, building, and optimizing the end-to-end machine learning platform that enables rapid development, deployment, and monitoring of ML models. We leverage cutting-edge technologies, including big data frameworks, real-time streaming, MLOps best practices, graph technologies, and Large Language Models (LLMs), to implement and improve our strategy.

Our team expands across Beijing, Shanghai and Singapore. We collaborate with other teams (Data Science, Product, Engineering) and our customers globally to ensure a holistic approach for risk management and deliver state-of-the-art ML capabilities.

Responsibilities :

• Design, build, and maintain scalable and reliable data pipelines for ingesting, processing, and transforming large datasets (batch and stream) for ML model training and inference.
• Develop and manage feature generation systems and feature stores, ensuring data quality, consistency, reusability, and accessibility for model development.
• Architect and implement robust model serving infrastructure for deploying, managing, and monitoring machine learning models in production at scale, ensuring low latency and high availability.
• Collaborate with Data Scientists, ML Engineers, and Senior Technical Staff to understand ML requirements and translate them into robust platform capabilities and infrastructure.
• Champion and implement MLOps best practices, including CI / CD for ML, model versioning, experiment tracking, automated monitoring, and feedback loops.
• Experiment with new technologies and frameworks in the big data, streaming, and MLOps space, proposing architectural improvements for the ML platform.
• Support and mentor less-experienced team members in ML platform engineering, big data technologies, data pipeline development, and MLOps practices.
• Ensure the security, scalability, performance, and cost-effectiveness of the ML platform components.

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 :

Preferred qualifications :

Tell employers what skills you have

Machine Learning

Version Control

Scalability

Scala

Azure

Big Data

Pipelines

Architectural

SageMaker

Hadoop

Software Engineering

Data Engineering

Python

Docker

Data Science

Java