Key skills
Machine Learning EngineeringPythonML PipelinesAWSSQLML LibrariesAIMachine LearningMLPyTorchscikit-learnPandasMLOpsKubeflowData EngineeringSparkAirflowdbtDockerECSEC2S3SaaSCI/CDCommunication
About this role
Nearmap is a SaaS company dedicated to innovation, particularly in the realm of imagery and analysis. They are seeking a Machine Learning Engineer to support their Insurance AI team by building and maintaining ML infrastructure, ensuring that Data Scientists have the tools necessary to deploy models effectively.
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
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
- 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