About Nearmap Property intelligence is reshaping how the world understands the built environment, and Nearmap is driving that. Backed by Space Capital.
About the role
This role is all about translating R&D from other parts of the Nearmap AI & Computer Vision team into data and ultimately, answers. You will be a core contributor to the algorithmic systems that create the Map Data behind Nearmap's AI products. Every time Nearmap flies a survey, we find and measure what's on the ground (roofs, trees, roads and much more) and turn it into foundational map data: clean, property-level data that other products build on. Found on 1752vc Careers, the job board for startup and VC roles.
What they're looking for
- You're pragmatic. You value theory and best practice, but you'd rather ship something that works than build something elaborate
- You're a team player. You communicate well, share what you know, and take good ideas from anyone in the team
- You're eager to learn. You bring real-world experience and still expect to pick up new things
- Education : Bachelor’s in Computer Science, Engineering, Statistics, Physics or a related field (Master’s or PhD a bonus, not required)
- Experience : 5+ years in a numerical field such as Applied Mathematics, Physical Sciences, Computer Vision or Geospatial Analysis
- Production Code : proven ability to write high-quality production numerical code with a focus on business outcomes
More about this role
This role is all about translating R&D from other parts of the Nearmap AI & Computer Vision team into data and ultimately, answers. You will be a core contributor to the algorithmic systems that create the Map Data behind Nearmap's AI products. Every time Nearmap flies a survey, we find and measure what's on the ground (roofs, trees, roads and much more) and turn it into foundational map data: clean, property-level data that other products build on. We work with 2D and 3D aerial imagery captured daily across whole countries, alongside property boundaries, licensed open data and other geospatial sources. We get that data into the hands of the people and systems that rely on it, and keeping it affordable and trustworthy only gets more interesting as the volume grows.
We design, build, and operate software systems that take petabytes of data to transform aerial imagery to insight. The team focuses on algorithmic systems, where each node of the computational graph is a software application in its own right (such as a deep learning model or geospatial algorithm), and the goal is to produce outstanding high-quality data for customers and internal stakeholders in a fully automated and...
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