Senior Software Engineer - Digitisation

About the role

Imagine having the power to stress-test an entire power grid against a hurricane or thunderstorm before the clouds even gather. That is the reality we are creating at Neara.

We use advanced machine learning to create engineering-grade, physics enabled digital twins of electricity grids across four continents, this helps asset owners understand their biggest challenges and bring the most viable solutions to life across millions of kilometres of infrastructure.

By simulating extreme weather and structural stress at a network-wide scale, we empower the world’s largest utilities to pinpoint risks, optimise investments and build a more resilient global energy future.

Our team is a collection of brilliant minds who are fanatical about making a tangible difference in the real world, utilising AI and machine learning to accelerate everything from data classification to complex scenario analysis. We have built a special culture where innovation thrives because everyone owns the mission and we need smart, creative people to help us scale this impact to every corner of the globe.

ABOUT THE ROLE

As a Senior Software Engineer in our Digitisation group, your goal is to make building digital twins self-service.

Our digital twins are built from LiDAR, imagery and other data sources using a mix of machine learning and algorithms, and then refined by our global delivery teams. You’ll build the platform that sits between those algorithms and the people who deliver with them. When you succeed, a user can:

- Run the build without help: Take the process from start to finish. When a step fails, they can see what went wrong and unblock it without a developer.

- Fix what the algorithms got wrong: A clean run doesn’t guarantee a correct model. Users can find where the output is off, fix it quickly and confirm it’s right before they finish. Several people can work on the same model at once without conflict.

- Know where they stand: Users see machine time, human correction time, processing errors and output quality at every stage. They get an early forecast of final throughput and quality, early enough to act on it.

Our stack spans a Dart-based client application, high-performance C++/Rust computation and simulation engines, and a Java backend for large-scale data services and APIs. Whether your own background is front end, backend, or systems, what matters is that you can earn credibility with engineers working in unfamiliar territory and know which technical trade-offs are worth your attention.

WHAT YOU WILL OWN

- The interface between the algorithms and the people who use them - Model quality, before the model reaches any downstream use case. Surfacing problems clearly enough that non-developers can act on them, and feeding human corrections back into our ML systems as training and evaluation data

- The frameworks that let this scale - Breaking work into standard, repeatable units that are measured the same way across projects and regions, so they can be forecast