Senior Software Engineer, Machine Learning Platform Engineering Engineering • Singapore APAC, Singapore Singapore

About Airwallex

Airwallex is the AI-native financial operating system for a real-time, intelligent economy. More than 675,000 businesses, including McLaren Racing, Qantas, SHEIN, and TikTok, use us, directly or through our platform partners, to run their financial operations or build and monetize financial products of their own.

We started in Melbourne in 2015 to build the infrastructure global commerce runs on. We're the regulated backbone behind global payments: not by accident, but by design. A decade plus, 85+ licenses, and a financial infrastructure spanning North America, Europe, the Middle East, and Asia-Pacific.

We're co-headquartered in San Francisco and Singapore, with more than 2,300 people across 27 offices. We hire builders with founder-level energy, people who move fast with good judgment, dig in with real curiosity, and make calls from first principles rather than waiting to be told what to do. Read our operating principles to see it in full.

About the team

Risk Platform builds the decisioning infrastructure that sits between Airwallex and every dollar that moves through it, protecting 150,000+ businesses moving over US$260 billion a year across 150+ countries and 60+ currencies, and deciding, often in milliseconds, whether a new signup is real, a payment is safe, or a login is who they claim to be. The hard part is that fraud evolves fast, and every decision carries a two-sided cost: miss an attack and money is lost, over-block and a legitimate business can't get paid. We build this with streaming pipelines processing billions of events a day, graph databases exposing coordinated fraud rings, ML models scoring every transaction, and LLM agents that triage alerts. You don't need a fintech background, just an appetite for adversarial systems problems where the scoreboard is measured in dollars. If you want to help scale one of the world's fastest-growing financial platforms safely, this is the team.

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 core machine learning platform focused on model training, fine-tuning, continuous evaluation, and high-performance inference serving. We leverage cutting-edge MLOps practices, model acceleration frameworks, and Large Language Models (LLMs) to power real-time risk decisioning.

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:

• Responsible for end-to-end model lifecycle management and performance optimization from training to serving (including model training, fine-tuning, continuous evaluation, and high-performance inference serving), leveraging MLOps practices, model acceleration frameworks, and Large Language Models (LLMs).
• Design, build, and scale the robust infrastructure supporting advanced machine learning models, enabling rapid iteration and deployment of risk/business strategies.
• Apply common performance analysis and optimization techniques to drive performance improvements, latency reduction, and cost efficiency across model training and inference workloads.
• Collaborate closely with Data Science, Product, and Engineering teams globally to deliver state-of-the-art, scalable ML capabilities.

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:

1. 5+ years of software engineering experience, with at least 3+ years focused on model training infrastructure, model serving systems, or MLOps platforms.
2. Bachelor's or Master's degree in Computer Science, Engineering, or a related technical field.
3. Hands-on experience with modern deep learning frameworks (e.g., PyTorch, TensorFlow, JAX) and model training execution engines.
4. Strong proficiency in core programming languages such as Python, Java, or C++.
5. Experience with distributed orchestration and workflow management tools (e.g., Kubernetes, Ray, Kubeflow Pipelines, Airflow).
6. Solid understanding of GPUs, including GPU architecture, hardware acceleration, and GPU-based training or inference optimization.

Preferred qualifications:

• Experience with model acceleration frameworks and Large Language Models (LLMs).
• Proficiency in performance profiling and bottleneck identification using tools like NVIDIA Nsight Systems for training and inference optimization.
• Experience with cloud platforms (e.g., AWS, GCP) and building large-scale, low-latency produ