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.

As a Senior Machine Learning Engineer, you will create machine learning models that drive the digitisation of real-world infrastructure from a variety of data sources such as LIDAR, imagery, and vector data sources. Working at every stage of the ML lifecycle, from data collection and QA to training to model monitoring, you’ll dive deep into the data, decide which problems are worth solving with ML and define the ML strategy for said problems.

This will involve staying updated with best practices in data handling, MLOps, as well as the latest advancements in machine learning to integrate new techniques into our platform, ensuring it remains at the cutting edge of the industry. You will also provide mentorship to junior engineers, fostering a collaborative and innovative work environment that encourages continuous learning and growth.

At Neara, you will be part of a team that values your ideas, supports your development, and celebrates your successes. If you are motivated and results-driven, we’d like to meet you.

Some of the things you will contribute to:

• Developing approaches to generate accurate electric networks from imperfect data using deep learning and classical ML algorithms

• Developing and optimising training pipelines to reduce toil and idea-to-evaluation time.

• Scaling the serving of models for different problems with different characteristics while reducing the time from the first model to production.

• Improving the speed of model QA and identifying data and distribution drift in the models.

• Working with a variety of data sources, including LiDAR, aerial photography, photogrammetry & GIS, and building scalable data pipelines to support training and serving.

• Mentoring other engineers in best practices for model training and software engineering.

Some attributes we highly regard:

• Demonstrated experience with Machine Learning and Deep Learning

• Strong foundation in software engineering

• Proficiency in frameworks like PyTorch/Tensorflow/Jax

• Excellent problem-solving skills as applied to new domains.

• Ability to work effectively asynchronously and cross-functionally on ill-defined problems.

• Ability to own problems and proactively approach challenges

Some of the benefits we have on offer:

• Competitive salary

• Meaningful ESOP

• Fully Flexible Work Environment. We have a fully stocked office (and an impressive snack collection) in Redfern.

• Regular office events

• The real benefit is working on a genuinely complex, innovative and industry-leading product, making a genuine difference in the world around us

To apply, please use the online application link below. Neara values diversity, belonging and equal employment opportunities. We encourage individuals from all backgrounds to apply.

Please note that you will require current and unrestricted working rights to be considered for the role. Unfortunately, we are not in a position to offer visa sponsorship at this time.

No agencies or third-party service providers, please.
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