Staff Machine Learning Engineer (Platform), Australia-based, Full Relocation Provided
Confirmed on the employer's own hiring board on Oct 26, 2026. First seen by Alion on Jul 14, 2026. Neara scores B on the Alion truth index.
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.
This role is located in Sydney, Australia - A relocation package and visa sponsorship will be provided as part of the salary package.
The Staff Machine Learning Platform Engineer owns the infrastructure and systems that allow Neara's ML discipline to move fast, ship reliably, and scale without breaking.
Neara is conducting cutting edge research, developing multi-modal spatial frontier models. You will help the team run faster, helping overcome challenges that have never been seen before in the world. These models work with a range of less researched data types, including point cloud, geospatial data, and asset data. The lack of research maturity in the geospatial domain and novel nature of the problem presents unique challenges around performance, data unification, and deployment.
The problem and role stretch beyond pure research. These models will be deployed with our global utility and new vertical customers, delivering real value and increased climate resilience for critical infrastructure. Your role will be critical in both making sure we can develop frontier level spatial intelligence quickly and economically, but also in making sure we can deploy those models efficiently to our customers.
WHAT YOU’LL DO
Own the ML platform strategy end-to-end - Define and drive the multi-year technical roadmap for training pipelines, serving architecture, experiment management, and monitoring systems that tie it all together.
Build tooling that accelerates ML delivery - Develop foundational infrastructure that takes engineers from idea to production faster, standardising workflows and eliminating friction between experimentation and deployment.
Solve hard distributed systems problems - Enable training across distributed data with residency and security requirements, while ensuring models run efficiently across varied GPU hardware, including sparse tensor implementations and architecture bottlenecks.
Design scalable, flexible serving architecture - Define serving systems that handle spiky load in production while giving the ML team the freedom to experiment across regions, customers, tasks, and verticals.
Unblock the ML team at scale - Identify what's slowing the team down, define the contracts and interfaces between training, evaluation, and serving, and build the roadmap to turn ambitious research into routine delivery.
WHAT YOU’LL BRING
A foundation in R&D to help drive the right direction and prioritisation necessary for faster iteration.
Demonstrated ability to set ML platform standards and interactions across teams, influence engineering roadmaps without direct authority, and drive alignment on complex infrastructure decisions.
Significant technical experience running deep learning at scale, with a track record of designing and operating the systems other ML engineers depend on.
Experience in building training data warehouses as well as bringing data systems to ML readiness.
Deep hands-on expertise building ML infrastructure at scale, in particular: training pipelines, distributed compute, model serving and model monitoring.
Deep familiarity with model monitoring, data quality frameworks, and the operational practices required to maintain a diverse portfolio of production ML models.
A proven investment in building others through documentation, internal standards, and raising the MLOps capability of the engineering discipline around you.
Strong proficiency in Python, PyTorch (or equivalent framework) and a passion for deep learning.
Demonstrated software engineering fundamentals across system design, code quality, and scalability, with a clear instinct for where to invest complexity and where to keep things simple.
Solid experience with cloud infrastructure (AWS, GCP, or Azure) and container orchestration (Kubernetes, Docker) as well as dealing with custom on-prem/neocloud offerings
Proficiency in writing and optimising custom CUDA kernels for deep learning training is a nice-to-have but not imperative
WHAT’S IN IT FOR YOU?
Full relocation to Australia
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.
No agencies or third-party service providers, please.