Staff Machine Learning Engineer (Platform)
Remote · Sydney, New South Wales, Australia
Job Summary
Staff Machine Learning Platform Engineer at Neara leads the ML platform strategy end-to-end, defines the multi-year roadmap for training pipelines, serving architecture, experiment management, and monitoring. Builds tooling to accelerate ML delivery, standardises workflows between experimentation and deployment, and solves distributed systems challenges to enable scalable training across heterogeneous GPUs. Designs scalable serving architectures that handle varying loads and supports collaboration across teams to turn ambitious research into routine delivery. The role requires hands-on experience with training pipelines, distributed compute, model serving, CUDA kernels, production ML monitoring, Python and PyTorch, and cloud/on-prem environments, plus mentoring ML engineers and driving technical standards.
Required Qualifications
- Significant hands-on experience building and operating training pipelines, distributed compute, model serving, and monitoring systems at scale.
- Strong proficiency writing and optimising custom CUDA kernels for deep learning training, ideally across non-text/image data types, with experience diagnosing performance bottlenecks in sparse architectures.
- Proven experience across production model monitoring, data quality frameworks, and training data warehouses brought to ML readiness.
- Demonstrated ability to set ML platform standards, influence engineering roadmaps, and drive alignment on complex infrastructure decisions across teams without direct authority.
- Proficiency in Python and PyTorch (or equivalent), strong system design instincts, and an R&D foundation that informs good prioritisation decisions.
- Experience across AWS, GCP, or Azure, container orchestration (Kubernetes, Docker), and on-prem or neocloud environments.
- A track record of leading, mentoring, and coaching ML engineers, raising the technical bar of the teams around you through hands-on guidance, documentation, and internal standards.
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