Senior Machine Learning Engineer – Geospatial AI Pipelines

Nearmap; Sydney; Hybrid; AUD 180,000 - 240,000; Full time

Benefits offered by this job

Hybrid flexibility

YOU days off

Parental leave

Nearmap subscription

Office lunches in Sydney

Job summary

Nearmap, a property intelligence company in Sydney, seeks a data/ML engineer to translate R&D from the AI & Computer Vision team into production data and map data. You will build and operate pipelines that transform aerial imagery into property-level data across large-scale geospatial datasets.

You’ll work with Python, Linux, and Git, collaborating with ML researchers and geospatial experts to deliver accurate, scalable solutions and improve performance across the country.

Qualifications

• Bachelor in CS/Engineering/Statistics/Physics or related field.
• 5+ years in numerical fields such as CV or Geospatial Analysis.
• Proven ability to write high-quality production numerical code.
• Experience with Scientific Python, Linux, Git.
• Strong ML fundamentals and statistical validation.
• Ability to build scalable ML systems.
• Strong systems thinking with pragmatic approach.

Responsibilities

• Translate R&D from the AI/CV teams into production data and answers.
• Design, build and operate pipelines processing petabytes of data.
• Collaborate with ML researchers to productionize models.
• Develop geospatial algorithms to create property-level map data.
• Ensure reliability, performance and cost-efficiency of pipelines.
• Review code and share knowledge to keep Python code clean and documented.

Skills

Production code

ML fundamentals

Geospatial analysis

Systems thinking

Team collaboration

Education

Bachelor’s in Computer Science or related field

Master’s or PhD a bonus

Tools

Scientific Python

Linux

Git

Geospatial libraries (GEOS, Shapely, GeoPandas)

Job description

Nearmap, a property intelligence company in Sydney, seeks a data/ML engineer to translate R&D from the AI & Computer Vision team into production data and map data. You will build and operate pipelines that transform aerial imagery into property-level data across large-scale geospatial datasets.

You’ll work with Python, Linux, and Git, collaborating with ML researchers and geospatial experts to deliver accurate, scalable solutions and improve performance across the country.