Full-Time Machine Learning Engineer

• Act as the Insurance AI team’s champion for code and tool reuse (python), ensuring models move smoothly from concept to reliable operation in production, and helping the team leverage “Nearmap scale” data effectively to build and deploy the best models.
• Design and build data and model pipelines that enable the full lifecycle of model development, testing, deployment, and monitoring.
• Adapt, extend, and productionize tooling from the broader AI & Computer Vision (AICV) team in Sydney to meet the specific needs of the Insurance AI team.
• Operate within cloud-based infrastructure, making use of internal and external APIs, bucket storage, databases, cloud compute and other technologies to integrate data, models, and workflows into scalable production systems.
• Ensure our infrastructure and processes support experimentation at speed while maintaining high standards for reliability, security, and scalability.

1. ML Engineering & Infrastructure Development
   Contribute to ML pipelines on cloud native technologies for data ingestion, feature processing, model training, deployment, and monitoring in AWS. Support internal tools and frameworks to streamline experimentation, ensure reproducibility, and improve delivery speed.
2. API Integration & Tooling Adaptation
   Develop, consume, and integrate both internal and external APIs to connect datasets, models, and services in collaboration with other engineers. Adapt and extend core tooling from the AI & Computer Vision team for Insurance AI’s specific use cases. Bridge the gap between research prototypes and production-grade systems.
3. Collaboration & Technical Leadership Serve as a trusted technical partner to data scientists, enabling them to execute modeling projects efficiently and at scale.

• Python-based Machine Learning: Using a range of python packages for ML pipeline development and deployment (such as sklearn, pandas, PyTorch).
• Software Development: ability to code in Python, with strong skills in writing clean, maintainable, well tested and efficient code, working with other engineers on a shared codebase.
• Data Engineering: Proficiency with SQL and experience building scalable data processing workflows (e.g., Apache Spark, Airflow, dbt).
• Collaboration: Strong communication skills and ability to translate technical solutions into actionable steps for non-engineers (working with a range of Data Scientists, ML Engineers and ML Ops engineers)

• Cloud Development Skills: Proficiency in a cloud based environment (ideally AWS).
• REST APIs: Experience consuming, and integrating with both internal and external APIs at scale.
• Containerisation and Environments: Working in a containerised environment such as Docker, and managing python dependencies and versions.
• MLOps: Working within a robust ML Ops framework, implementing CI/CD and model monitoring for deployed models.
• Familiarity with property/casualty insurance space.
• Familiarity with ML pipelining tools such as Ray, Kubeflow, Flyte.
• Experience with geospatial or imagery data processing (using geopandas, working with spatial data and transforms).

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