Senior Machine Learning Engineer (AI & Computer Vision)
Remote · AU
Job Summary
Senior ML Engineer role focused on translating R&D from Nearmap's AI & Computer Vision work into robust, production-ready conflation pipelines for geospatial mapping. You will work with Python (scientific stack: NumPy, SciPy, PyTorch), GIS tools (GEOS, Shapely, GeoPandas/GeoPolars), and large aerial-imagery datasets to produce interpretable map data with clear data provenance. Expect pair programming, collaboration with CV researchers, and reviews of model components and spatial indexing approaches. The role emphasizes building scalable machine learning systems, clean production code, and rigorous validation within a cloud-enabled environment. Fully remote and open to candidates located on the East Coast of Australia (Melbourne, Sydney, Brisbane); requires strong numerical reasoning, pragmatic engineering, and a focus on delivering robust, reproducible results.
Required Qualifications
- Bachelor's degree in computer science, engineering, statistics, physics, or related field
- Demonstrated 5+ year history in a numerical field (e.g., applied maths, physical sciences, computer vision, geospatial analysis)
- Proven ability to produce high-quality production numerical code in Python
- Experience with Linux and Git
- Understanding of ML fundamentals, regularisation, hyperparameter optimization, and validation methods
- Experience with geospatial data and GIS tools
- Strong software engineering practices for scientific Python
- Ability to work in a remote team environment and communicate effectively
Desired Qualifications
- 5+ years experience in numerical field
- Bachelor's degree in computer science, engineering, statistics, physics, or related field
- Masters/PhD welcomed but not required
- Strong Python coding in a Linux environment
- Experience with PyTorch or similar ML frameworks
- Experience with geospatial tools (GEOS, Shapely, GeoPandas/GeoPolars)
- Experience handling large datasets and scalable ML systems
- Good communication and collaboration skills
- Experience with cloud-native environments
- Strong software engineering practices for scientific Python
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