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Airwallex is an AI-native financial operating system serving over 675,000 businesses including McLaren Racing, Qantas, SHEIN, and TikTok. The company operates as the regulated backbone for global payments, with 85+ licenses and infrastructure spanning North America, Europe, the Middle East, and Asia-Pacific. Co-headquartered in San Francisco and Singapore with 2,300+ employees across 27 offices.
In this role, you will build and scale the machine learning platform powering risk decisioning across Airwallex. You'll design reliable data and model infrastructure, productionize machine learning workflows, and improve experimentation speed and quality across the Risk Platform. The Risk Platform team builds decisioning infrastructure protecting 150,000+ businesses moving over $260 billion annually across 200+ countries and 90+ currencies, processing billions of events daily through streaming pipelines, graph databases, ML models, and LLM agents.
You'll partner closely with machine learning engineers, data scientists, product managers, and risk specialists to transform complex fraud and risk problems into dependable systems. The role requires owning outcomes, moving fast with good judgment, and working comfortably with ambiguity. You'll collaborate across multiple time zones as part of a global team and be based in Singapore working from the office five days per week.
REQUIREMENTS:
- 5+ years of software engineering experience, with at least 3+ years focused on model training infrastructure, model serving systems, or MLOps platforms
- Bachelor's or Master's degree in Computer Science, Engineering, or related technical field
- Hands-on experience with modern deep learning frameworks (PyTorch, TensorFlow, JAX)
- Strong proficiency in Python, Java, or C++
- Experience with distributed orchestration and workflow management tools (Kubernetes, Ray, Kubeflow Pipelines, Airflow)
- Solid understanding of GPUs, including architecture, hardware acceleration, and GPU-based training/inference optimization
PREFERRED:
- Experience with model acceleration frameworks and Large Language Models (LLMs)
- Proficiency in performance profiling and bottleneck identification (NVIDIA Nsight Systems)
- Experience with cloud platforms (AWS, GCP) and large-scale, low-latency production ML infrastructure
