Senior Machine Learning Engineer (Platform)

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.

The Senior MLOps Engineer builds and runs the pipelines, deployment systems, and observability that keep Neara's ML models training reliably and serving in production.

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 getting models out of the lab and into customer environments - reliably, repeatably, and economically.

WHAT YOU'LL DO

- Build and operate ML pipelines - Own the training, evaluation, and deployment pipelines end-to-end, from data ingestion through to models running in customer environments.

- Automate the path to production - Build CI/CD for models, artefact and model registries, reproducible environments, and infrastructure as code that takes an experiment to a deployed service without manual steps.

- Keep production models healthy - Implement monitoring, alerting, and drift and data quality checks, and act as a first responder when something breaks.

- Run distributed training infrastructure - Manage GPU clusters and scheduling, keep jobs efficient and utilisation high, and troubleshoot failures across cloud, on-prem, and neocloud environments.

- Ship reliable serving infrastructure - Deploy and scale inference services that handle spiky load across regions and customers, with attention to latency, cost, and data residency requirements.