18 Jun 2026

Staff Machine Learning Engineer (Platform)

Neara

Sydney

18 Jun 2026

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Neara is seeking a Staff Machine Learning Platform Engineer to own the infrastructure and systems driving ML development in Sydney. This role involves building tooling, solving distributed systems problems, and designing scalable serving architecture for multi-modal spatial models. The ideal candidate brings deep experience in ML platforms, CUDA optimization, and cloud infrastructure.

Core AIOn-siteFull-timePrincipalPythonPyTorch

Staff Machine Learning Engineer (Platform)

Apply

Core AIOn-siteFull-timePrincipalPython

Salary

Not specified

Work Location

Sydney, New South Wales, Australia, AU

Work Model

On-site

Employment Type

Full-time

Experience Level

Staff

Core Qualifications

Technical (Must-have)

PythonPyTorchCUDAAWSGCPAzureKubernetesDockerMachine LearningDeep LearningDistributed SystemsModel ServingData Quality Frameworks

Soft Skills

LeadershipMentoringCoachingCommunicationProblem SolvingStrategic Thinking

Key Responsibilities

•Own the ML platform strategy end-to-end, defining and driving the multi-year technical roadmap for training pipelines, serving architecture, experiment management, and monitoring systems.
•Build tooling that accelerates ML delivery, developing foundational infrastructure to standardise workflows and eliminate friction between experimentation and deployment.
•Solve hard distributed systems problems, enabling training across distributed data with residency and security requirements, while ensuring efficient model performance across varied GPU hardware.
•Design scalable, flexible serving architecture that handles spiky load in production and enables ML team flexibility across regions, customers, tasks, and verticals.
•Unblock the ML team at scale by identifying slowdowns, defining contracts and interfaces between training, evaluation, and serving, and building roadmap from research to delivery.

Staff Machine Learning EngineerML PlatformMachine LearningCUDAPyTorchKubernetesAWSGCPAzureGeospatial AI

Key Responsibilities

•Own the ML platform strategy end-to-end, defining and driving the multi-year technical roadmap for training pipelines, serving architecture, experiment management, and monitoring systems.

•Build tooling that accelerates ML delivery, developing foundational infrastructure to standardise workflows and eliminate friction between experimentation and deployment.

•Solve hard distributed systems problems, enabling training across distributed data with residency and security requirements, while ensuring efficient model performance across varied GPU hardware.

•Design scalable, flexible serving architecture that handles spiky load in production and enables ML team flexibility across regions, customers, tasks, and verticals.

•Unblock the ML team at scale by identifying slowdowns, defining contracts and interfaces between training, evaluation, and serving, and building roadmap from research to delivery.