Staff ML Platform Engineer: Scale & Deploy AI for Grids

Matchbox; Sydney; Hybrid; AUD 180,000 - 240,000; Full time

Benefits offered by this job

Competitive salary

Meaningful ESOP

Flexible work environment

Office in Redfern

Regular office events

Job summary

Neara is seeking a Staff ML Platform Engineer to own the infrastructure enabling fast, reliable, scalable ML delivery across geographies. You’ll shape training pipelines, serving architecture, and monitoring while guiding cross‑team choices.

You’ll work on multi‑modal spatial models with geospatial data and point cloud, deploying solutions for global utilities. The role blends research with production, delivering real climate resilience through cutting‑edge technology.

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.

Responsibilities

• Own the ML platform strategy end-to-end - Define and drive the multi-year technical roadmap for training pipelines, serving architecture, experiment management, and monitoring systems that tie it all together.
• Build tooling that accelerates ML delivery - Develop foundational infrastructure that takes engineers from idea to production faster, standardising workflows and eliminating friction between experimentation and deployment.
• Solve hard distributed systems problems - Enable training across distributed data with residency and security requirements, while ensuring models run efficiently across varied GPU hardware, including sparse tensor implementations and architecture bottlenecks.
• Design scalable, flexible serving architecture - Define serving systems that handle spiky load in production while giving the ML team the freedom to experiment across regions, customers, tasks, and verticals.
• Unblock the ML team at scale - Identify what's slowing the team down, define the contracts and interfaces between training, evaluation, and serving, and build the roadmap to turn ambitious research into routine delivery.

Skills

Python

PyTorch

Distributed systems

CUDA optimization

Leadership

Tools

Kubernetes

Docker

AWS/GCP/Azure

CUDA kernels

PyTorch serving

Job description

Neara is seeking a Staff ML Platform Engineer to own the infrastructure enabling fast, reliable, scalable ML delivery across geographies. You’ll shape training pipelines, serving architecture, and monitoring while guiding cross‑team choices.

You’ll work on multi‑modal spatial models with geospatial data and point cloud, deploying solutions for global utilities. The role blends research with production, delivering real climate resilience through cutting‑edge technology.