Machine Learning Engineer

Nearmap•Carlsbad, CA

About The Position

Nearmap is seeking a Machine Learning Engineer to join their Insurance AI team. This role will serve as the engineering backbone for Data Scientists, focusing on building and maintaining the ML infrastructure that transforms models into reliable, scalable products. The position involves extending, adapting, and maintaining existing ML tooling and pipelines, originally developed by the Sydney-based AI & Computer Vision team, for US-specific use cases. The ideal candidate will find satisfaction in improving existing systems rather than starting from scratch. This role requires close collaboration with US-based Data Scientists and Australian ML Engineers, acting as a technical bridge between these teams.

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.

Nice To Haves

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.

Responsibilities

Build and maintain ML pipelines for data ingestion, feature processing, model training, deployment, and monitoring in AWS.
Extend and adapt existing tooling from the 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.