Property intelligence is reshaping how the world understands the built environment, and Nearmap is driving that. We put powerful aerial imagery, AI-driven analytics, and geospatial tools into the hands of the people who plan, build, insure, and govern the places we all live and work. Our technology turns property uncertainty into decisive action, and our culture brings out the best in the people who build it.

The role

As a Machine Learing Engineer, you will support a team of Data Scientists with tooling, data transformation, and deploying realtime models on Ray Serve endpoints.

The team builds risk models and other related scores, using geospatial data as inputs to identify damage, rate the quality of a roof, or predict the likelihood of damage in the event of a natural disaster.

Your role is to support the Data Science team's end-to-end workflow, and to be the key bridge between them and our ML Ops teams and systems in Australia and Poland

What you'll be doing

• Supporting the deployment of new risk models and scores in production.
• Helping work with large scale data sets (hundreds of millions of rows), and building custom workflows on top of existing foundations.
• Set up Claude Code usage patterns to allow team members to work more easily with AWS / Ray / Docker / Linux tooling.
• Own data transformation pipelines that turn semantic geospatial maps of a property into attributes suitable for categorical modelling.
• Collaborating with engineers to ensure solutions will work reliably on our tens-of-petabyte scale data sets, with multi-date, multi-angle, multi-modal data as inputs to algorithms.

What we're looking for

You'll need:

• Around two years of industry experience writing Python in a professional software or ML engineering context
• A degree in Computer Science or a related technical field
• Hands-on experience working in a shared codebase: feature branches, pull request reviews, collaborative development with other engineers
• Strong Python fundamentals and a track record of writing clean, maintainable, well-tested code
• Solid data engineering skills, SQL, and experience with workflow tools like Airflow or Spark
• Practical experience with LLMs, whether integrating APIs, fine-tuning, prompt engineering, or building LLM-powered components
• Good communication, including the ability to talk to non-engineers without losing the technical plot

Nice to have:

• AWS experience (S3, EC2, ECS)
• Docker and containerised environments
• REST API integration at scale
• MLOps and CI/CD
• Familiarity with geospatial data
   

  Ways of Working

  This is a hybrid role based on the West Coast of the United States. You'll have regular in-office anchor days and the flexibility to work remotely the rest of the time. You'll collaborate closely with a distributed team spanning the US and Sydney.

  Why Nearmap

  We are a company that moves at speed, builds things that matter, and takes the quality of our engineering seriously. You'll have access to real data at real scale, and the support of a team that values pragmatism over theory and collaboration over silos.

  If you want to spend your time shipping work that counts, this is the role.

Why you'll love working at Nearmap:

We move fast and work smart; often wearing multiple hats. We adapted to remote working with ease and are continually looking at ways to improve. We’re proud of our inclusive, supportive culture, and maintain a safe environment where everyone feels a sense of belonging and can be themselves.

In addition to your annual leave, Nearmap offers:

• 4 extra "YOU" days off each year—take a break, no questions asked!
• Company-sponsored volunteering days to give back.
• Generous parental leave policies for growing families.
• Work from Overseas Policy - explore the world in the approved list of cities while you work!
• Access to LinkedIn Learning for continuous growth.
• Discounted Private Health Insurance plans.
• Monthly wellbeing and technology allowance.
• A Nearmap subscription (naturally!).

Learn More About The Work We Do

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