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Neara is building physics-enabled digital twins of electricity grids using advanced machine learning to help utilities stress-test infrastructure, optimize investments, and build climate resilience. The Senior Machine Learning Engineer (Platform) will own the end-to-end ML infrastructure that keeps Neara's models training reliably and serving in production across global utility and infrastructure customers.
You will build and operate ML training pipelines from data ingestion through deployment, automating the path from experiment to production service. This includes designing CI/CD systems for models, managing artifact and model registries, and creating reproducible environments using infrastructure as code. You'll implement comprehensive monitoring, alerting, and data quality checks to keep production models healthy, acting as a first responder when issues arise.
The role involves managing distributed GPU training infrastructure across cloud, on-premises, and specialized environments, optimizing cluster utilization and troubleshooting failures. You'll design and scale inference services that handle variable load across regions and customer environments, balancing latency, cost, and data residency requirements. A key part of the job is removing friction for the ML team by improving tooling, workflows, and documentation as the organization scales.
Neara's models work with novel data types including point cloud and geospatial data, presenting unique challenges around performance and data unification. The work bridges research and production—getting cutting-edge models out of the lab and into customer environments reliably and economically.
You'll bring solid hands-on experience building and operating production ML systems, strong Python skills, and working knowledge of PyTorch or equivalent frameworks. You need practical experience with distributed training, GPU infrastructure, cloud platforms (AWS, GCP, Azure), container orchestration (Kubernetes, Docker), and infrastructure as code. Experience with production model monitoring, data quality frameworks, and preparing training data for ML readiness is essential. Sound engineering judgment, maintainable code practices, and understanding of failure modes are critical. Bonus skills include CUDA optimization, exposure to point cloud or geospatial data, or experience supporting deployments in regulated or air-gapped environments.
Neara offers competitive salary, meaningful equity (ESOP), and a fully flexible work environment with a stocked office in Redfern. The real benefit is working on a genuinely complex, innovative product making a tangible difference in global energy resilience.
