Senior Machine Learning Engineer (Platform)

About Neara

We use advanced machine learning to create engineering-grade, physics-enabled digital twins of electricity grids across four continents. 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.

About the Role

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.

Your role will be critical in getting models out of the lab and into customer environments - reliably, repeatably, and economically.

Responsibilities

• 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.
• Remove friction for the ML team — Improve the tooling and workflows ML engineers use daily, and document the standards that keep things consistent as the team grows.

Requirements

• Solid hands-on experience building and operating ML training pipelines, model serving, and monitoring systems in production.
• Strong Python, and working knowledge of PyTorch (or equivalent) — enough to debug a training job, not necessarily to design the model.
• Practical experience with distributed training and GPU infrastructure, including scheduling, resource management, and diagnosing throughput or memory issues.
• Experience in the cloud (AWS, GCP, or Azure), container orchestration (Kubernetes, Docker), and infrastructure as code.
• Experience with production model monitoring, data quality frameworks, and preparing training data for ML readiness.
• Sound engineering judgement — you write maintainable code, think about failure modes, and know when a quick fix is fine and when it isn't.

Bonus:

• CUDA or kernel-level optimisation experience
• Exposure to point cloud or geospatial data
• Experience supporting deployments into regulated or air-gapped customer environments

Benefits

• Competitive salary
• Meaningful ESOP
• Fully flexible work environment. We have a fully stocked office (and an impressive snack collection) in Redfern.
• Regular office events
• Working on a genuinely complex, innovative and industry-leading product, making a genuine difference in the world around us

How to Apply

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.

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