Remote Senior ML Engineer – Production ML & MLOps

Pace XL Ltd.; Adelaide; On-site; AUD 150,000 - 230,000; Full time

Benefits offered by this job

Equity (ESOP) grants

20 days annual leave

Wellness days: 10 per year

Learning budgets

Employee Assistance Program

Job summary

Fleet Space Technologies is hiring a Senior Machine Learning Engineer to turn research models into production-grade products. You will own streaming data pipelines, GPU environments and the ML experiment platform, including MLflow tracking and model registry, while collaborating with data science and platform teams.

You will ensure leakage-safe evaluation, calibrated uncertainty, and reproducible model lineage from config to prediction, contributing new patterns and reusable modules for scalable

Qualifications

• Deep expertise in machine learning across modeling, engineering and architecture.
• Experience as a software engineer focused on ML and production-grade systems.
• Strong aligns with Python stack and MLOps tooling (MLflow, AWS, Databricks, Terraform).

Responsibilities

• Transition research models into production-ready products with quality and scalability.
• Build and maintain robust ML/data pipelines and GPU-enabled infrastructure.
• Own model quality across retrains with leakage-safe evaluation and calibrated uncertainty.
• Manage ML experiment platform: MLflow tracking and model registry.
• Collaborate with platform team and reuse shared infrastructure modules.
• Write clean Python with tests and automated end-to-end tests.
• Stay updated on ML and MLOps trends and bring best practices back.

Skills

Python

ML engineering

MLOps

AWS

Databricks

Terraform

GitLab

MLflow

Zarr

Icechunk

Tools

TensorFlow

PyTorch

MLflow

AWS

Databricks

Terraform

GitLab

Zarr

Icechunk

Job description

Fleet Space Technologies is hiring a Senior Machine Learning Engineer to turn research models into production-grade products. You will own streaming data pipelines, GPU environments and the ML experiment platform, including MLflow tracking and model registry, while collaborating with data science and platform teams.

You will ensure leakage-safe evaluation, calibrated uncertainty, and reproducible model lineage from config to prediction, contributing new patterns and reusable modules for scalable