Senior Software Engineer, Machine Learning Platform

Airwallex

. Build and scale the machine learning platform powering risk decisioning across Airwallex .

Posted 10/28/2026full-timeSeattle • Washington • United StatesSenior💰 $140 - $245 per hourWebsite

Core Competencies

Role fit

Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates expertise in building and scaling machine learning platforms, with a strong focus on model training infrastructure and MLOps. Proficient in developing reliable data and model systems to enhance risk decisioning and experimentation quality.

Highest-signal resume keywords

Machine Learning Platform DevelopmentModel Training InfrastructureProficiency in PythonExperience with KubernetesUnderstanding of GPU Architecture

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Machine LearningModel Serving SystemsMLOpsPyTorchTensorFlowJAXModel Training Execution EnginesPythonJavaC++

Tools & Technologies

KubernetesRayKubeflow PipelinesAirflowNVIDIA Nsight SystemsAWSGCP

Industry Keywords

Risk DecisioningFraud DetectionStreaming PipelinesGraph DatabasesLarge Language Models

Tech Stack

Tools & technologies

AirflowAWSGoogle Cloud PlatformJavaKubernetesPythonPyTorchRayTensorflowC++

About the role

Key responsibilities & impact

Build and scale the machine learning platform powering risk decisioning across Airwallex
Enable teams to develop, deploy, monitor, and improve models
Design reliable data and model infrastructure
Productionise machine learning workflows
Improve experimentation and decisioning speed and quality across the Risk Platform
Partner with machine learning engineers, data scientists, product managers, and risk specialists
Turn complex fraud and risk problems into dependable systems
Build systems using streaming pipelines, graph databases, machine learning models, and LLM agents for fraud and risk decisioning

Requirements

What you’ll need

5+ years of software engineering experience
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, and model training execution engines
Strong proficiency in Python, Java, or C++
Experience with Kubernetes, Ray, Kubeflow Pipelines, or Airflow
Understanding of GPUs, GPU architecture, hardware acceleration, and GPU-based training or inference optimization
Preferred: experience with model acceleration frameworks and Large Language Models (LLMs)
Preferred: proficiency with NVIDIA Nsight Systems
Preferred: experience with AWS or GCP and large-scale, low-latency production machine learning infrastructure
Ability to work from the Seattle office 3 days a week
Legally authorized to work in the United States

Benefits

Comp & perks

Equity
Bonus
Medical, dental, and vision insurance
401(k) plan
Short-term and long-term disability
Basic life insurance
Well-being benefits
20 paid days of vacation
12 paid days of company holidays
Statutory minimum paid annual leave and public holidays