Machine Learning Engineer
Role details
Job location
Tech stack
Job description
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., * Build and maintain ML pipelines for data ingestion, feature processing, model training, deployment, and monitoring in AWS
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Extend and adapt existing tooling from our Sydney AICV team for US Insurance AI use cases
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Develop and support internal tools and frameworks that streamline experimentation and improve delivery speed
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Integrate internal and external APIs to connect datasets, models, and services
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Partner with Data Scientists to understand their workflow needs and translate them into scalable technical solutions
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Ensure infrastructure supports rapid experimentation while maintaining reliability, security, and scalability
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Collaborate with Technical Product Managers, API engineers, and platform teams to deploy models in production
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Contribute to a shared codebase through feature branches, pull requests, and code reviews, You're mid-career and self-sufficient. You don't need someone looking over your shoulder, but you also know when to ask questions. You'd rather build on a solid foundation than start from scratch just to put your stamp on something. You communicate clearly, collaborate well with remote teams, and care about shipping things that actually work. To help us get to know the real you: In your application, tell us about a specific ML pipeline you've built or maintained and one thing you learned from it. Skip the AI-generated cover letters. We want to hear your voice. Additional Information 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:
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4 extra "YOU" days off each year-take a break, no questions asked!
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Company-sponsored volunteering days to give back.
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Generous parental leave policies for growing families.
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Work from Overseas Policy - explore the world in the approved list of cities while you work!
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Access to LinkedIn Learning for continuous growth.
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Discounted Private Health Insurance plans.
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Monthly wellbeing and technology allowance.
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A Nearmap subscription (naturally!).
Learn More About The Work We Do Thanks, but we got this! Nearmap does not accept unsolicited resumes from recruitment agencies and search firms. Please do not email or send unsolicited resumes to any Nearmap employee, location or address. Nearmap is not responsible for any fees related to unsolicited resumes.
Requirements
- 2-4 years as a Machine Learning Engineer or ML-focused Software Engineer
- Strong Python skills with a track record of writing clean, tested, production-grade code
- Hands-on experience with ML libraries like PyTorch, scikit-learn, and pandas
- Experience building and maintaining ML pipelines in production environments
- Solid SQL skills and familiarity with data engineering tools (Airflow, Spark, or dbt)
- The ability to jump into an existing codebase, understand it, and extend it
- Clear communication skills and comfort working across time zones
It would be great if you also have:
- AWS experience (S3, EC2, ECS, or similar)
- Experience consuming and integrating REST APIs at scale
- Docker and containerisation experience
- MLOps experience including CI/CD and model monitoring
- Familiarity with geospatial or aerial imagery data
- Experience with pipeline orchestration tools like Ray, Kubeflow, or Flyte