Senior Machine Learning Engineer

Responsibilities

• Design, build, and operate production pipelines that transform aerial imagery and geospatial data into property-level map data.
• Monitor pipeline runs, diagnose failures, and fix root causes to maintain reliability.
• Improve pipeline performance and cost through profiling, GPU utilization, distributed computing, and compiled code where appropriate.
• Measure data quality and validate model or pipeline changes against current outputs before release.
• Turn research models into production systems that run across whole countries.
• Develop geospatial algorithms for tasks such as stitching features across tile boundaries and clipping them to property parcels.
• Deliver data through batch outputs and lower-latency services.
• Review code, share knowledge, and maintain clean, tested, and documented scientific Python.
• Build tools that enable internal users to find and use Nearmap data and support those users.

Requirements

• Bachelor’s degree in Computer Science, Engineering, Statistics, Physics, or a related field; a master’s or PhD is a bonus but not required.
• 5+ years of experience in a numerical field such as applied mathematics, physical sciences, computer vision, or geospatial analysis.
• Proven ability to write high-quality production numerical code focused on business outcomes.
• Experience with scientific Python, Linux, and Git.
• Knowledge of modern software engineering practices, clean code, robustness, correctness, and scalable machine learning systems.
• Experience working with data too large for memory across multiple nodes on scalable cloud compute.
• Understanding of machine learning fundamentals including regularization, hyperparameter optimization, and validation methods.
• Ability to form hypotheses and validate them with statistical tests.
• Strong systems thinking combined with a pragmatic approach.
• Highly desirable: experience with cloud-native scalable computing, production operationalization of numerical applications and workflows, and machine learning applied to image and geospatial data.

Benefits

• Flexible time off and four additional annual “YOU” days or wellbeing days.
• Generous parental leave policies.
• Work from Overseas Policy for approved cities.
• Discounted private health insurance plans.
• Nearmap subscription.
• Wellbeing and technology allowance.
• Annual flu vaccinations.
• Hybrid flexibility for the role.
• Stocked kitchen with snacks.
• In-office lunch every Tuesday and Thursday at the Sydney CBD office.
• Showers available for employees cycling to work or exercising during the day.