Senior Software Engineer, Machine Learning Platform

About the role

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 2,700 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 https://www.airwallex.com/en-us/operating-principles to see it in full.

HOW YOU'LL MAKE IMPACT

You’ll build and scale the machine learning platform that powers risk decisioning across Airwallex, helping teams develop, deploy, monitor, and improve models that protect every dollar moving through our platform.

You’ll design reliable data and model infrastructure, productionise machine learning workflows, and improve the speed and quality of experimentation and decisioning across the Risk Platform.

You’ll partner closely with machine learning engineers, data scientists, product managers, and risk specialists to turn complex fraud and risk problems into dependable systems.

You’ll be based in Seattle and work from the office 3 days a week.

WHAT WE'RE LOOKING FOR

ESSENTIALS

- 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 a related technical field.

- Hands-on experience with modern deep learning frameworks (e.g., PyTorch, TensorFlow, JAX) and model training execution engines.

- Strong proficiency in core programming languages such as Python, Java, or C++.

- Experience with distributed orchestration and workflow management tools (e.g., Kubernetes, Ray, Kubeflow Pipelines, Airflow).

- Solid understanding of GPUs, including GPU architecture, hardware acceleration, and GPU-based training or inference optimization.

PREFERRED

- 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 production machine learning infrastructure.