Machine Learning Engineer

The sky’s not the limit at Nearmap

We’re a SaaS company, with proprietary hardware and software that’s continuously advancing through our commitment to innovation. The sky’s the limit when it comes to what we can and plan to do for our customers. Our imagery is just the starting point. Our impact comes from our people, applying complex analysis, interpretation and artificial intelligence that opens up all sorts of possibilities for our customers.

Job Description

The Insurance AI team conducts technical work to design, develop, and support products that use Nearmap and third-party data to derive insurance risk insights. While our data scientists focus on modeling and analysis, the Machine Learning Engineer ensures they have the robust, scalable, and efficient tools, pipelines, and development environments they need to deliver.

In this role, you will:

• 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 product ionize 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.

While prior experience in insurance or deep learning on aerial imagery is not required, familiarity in these areas will be considered a plus.

Key Responsibilities:

• 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.

Qualifications

• 2+ years of experience as a Machine Learning Engineer, ML-focused Software Engineer, or equivalent, with a proven record of delivering production-grade ML systems.
• Bachelors degree in math, computer science or other related technical field.
• Experience integrating and adapting existing ML tools to new domains or use cases.

Mandatory:

• 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. You will need to make use of a range of python packages built by other teams, and work collaboratively through feature branch and pull request reviews.
• 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)

Highly desirable:

• Cloud Development Skills: Proficiency in a cloud based environment (ideally AWS), including fundamental cloud technologies such as S3, EC2 and ECS, as well as using managed services such as Snowflake, Weights and Biases, or managed…