Machine Learning Engineer at Nearmap

About the Role We're looking for a Machine Learning Engineer to join our Insurance AI team. You'll be the engineering backbone for our Data Scientists, building and maintaining the ML infrastructure that turns models into reliable, scalable products. This isn't a greenfield build-everything-from-scratch role. Our Sydney-based AI & Computer Vision team has built robust ML tooling and pipelines. Your job is to extend, adapt, and maintain that infrastructure for US-specific use cases. If you're someone who gets satisfaction from making existing systems work better rather than reinventing the wheel, keep reading. You'll work closely with Data Scientists in the US and ML Engineers in Australia, acting as the technical bridge that keeps both teams moving fast. What You'll Do You'll own the ML engineering function for the US Insurance AI team. That means building data and model pipelines, integrating with internal and external APIs, and making sure our Data Scientists have the tools they need to ship models to production. You'll collaborate daily with our Sydney AICV team to leverage shared infrastructure and contribute improvements back. Day to day, you'll write Python, wrangle data pipelines, debug production issues, and translate Data Scientist requirements into working systems. You'll use AWS, work with cloud-native technologies, and operate within an established MLOps framework. Key Responsibilities • Build and maintain ML pipelines for data ingestion, feature processing, model training, deployment, and monitoring in AWS • Extend and adapt existing tooling from our Sydney AICV team for US Insurance AI use cases • Develop and support internal tools and frameworks that streamline experimentation and improve delivery speed • Integrate internal and external APIs to connect datasets, models, and services • Partner with Data Scientists to understand their workflow needs and translate them into scalable technical solutions • Ensure infrastructure supports rapid experimentation while maintaining reliability, security, and scalability • Collaborate with Technical Product Managers, API engineers, and platform teams to deploy models in production • Contribute to a shared codebase through feature branches, pull requests, and code reviews