Senior Software Engineer, Machine Learning Platform (MLOps)

Location: Singapore; office-based five days a week

Company

Airwallex operates an AI-native financial infrastructure platform for global payments, financial operations, and financial product development.

What you will do

• Build and scale the machine learning platform powering risk decisioning across the payment infrastructure.
• Design reliable data and model infrastructure for model development, deployment, monitoring, and improvement.
• Productionize machine learning workflows and improve experimentation and decisioning speed and quality.
• Develop infrastructure for large-scale, low-latency machine learning systems, including training and inference optimization.
• Partner with machine learning engineers, data scientists, product managers, and risk specialists on fraud and risk systems.

Requirements

• 5+ years of software engineering experience, including 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 PyTorch, TensorFlow, JAX, or similar deep learning frameworks and model training execution engines.
• Strong proficiency in Python, Java, or C++.
• Experience with Kubernetes, Ray, Kubeflow Pipelines, Airflow, or comparable distributed orchestration and workflow management tools.
• Solid understanding of GPU architecture, hardware acceleration, and GPU-based training or inference optimization.

Nice to have

• Experience with model acceleration frameworks and Large Language Models.
• Performance profiling and bottleneck identification using tools such as NVIDIA Nsight Systems.
• Experience with AWS or GCP and large-scale production machine learning infrastructure.

Culture & Benefits

• Ownership of the roadmap and outcomes in an environment that supports independent decision-making.
• Close collaboration with colleagues across multiple countries and time zones.
• In-person collaboration in the Singapore office five days per week.
• Occasional flexibility to adjust working hours for cross-time-zone collaboration.