ML Platform & Infrastructure
The engineers who make everyone else’s models real: training and inference infrastructure, MLOps, GPU economics, AI security.
A$170k – A$300k package
Every dollar of AI ambition eventually lands on a platform team. Feature stores, training pipelines, model registries, deployment, observability, inference cost — these roles decide whether an AI strategy compounds or stalls. They are also the hardest AI roles to assess with a generalist lens, because the craft is invisible in a CV: two candidates with identical keywords can be years apart in real capability.
Australia’s platform talent pool is thin and heavily contested — these people are rarely on the market, and when they are, they move through networks, not job ads. Our desk maintains a mapped, continuously interviewed network across the platform community, so a search starts from known, assessed people rather than from an advertisement.
Assessment goes deep on systems judgement: scaling decisions and their costs, reliability under real traffic, the boundary between platform and product, and — increasingly — inference economics, where a single good hire can repay their package in GPU savings alone.
- ML Platform Engineer
- MLOps Engineer
- Inference / GPU Infrastructure Engineer
- ML Systems Engineer
- AI Security Engineer
Where this hiring goes wrong
ML platform ≠ DevOps with a new title
Model lifecycles break standard infra assumptions — non-determinism, drift, GPU scheduling, evaluation gates. Strong platform engineers speak both fluently; retitled DevOps engineers usually cannot.
Inference is its own discipline now
Serving economics — batching, quantisation, caching, hardware choice — has become a specialist skill worth its own role in any company running models at scale.