Principal Machine Learning Engineer – FinTech (Sydney) ASX-Listed Hybrid
Up to $200k base + Super + Bonus + ESOP
A high-growth, product-led FinTech is looking for a Principal Machine Learning Engineer to shape, build, and scale their next generation of ML products – from real-time decisioning and risk models to personalised customer experiences.
Why this role is exciting
* Own the ML engineering vision for a modern B2C financial product
* Lead technically while staying hands-on in code
* Build on modern tooling: Python, cloud, Databricks, MLflow, event-driven architecture
What you'll do
* Set the ML engineering standards – model development, deployment, monitoring, and experimentation
* Design & build production ML systems – batch and real-time pipelines across credit, risk, fraud, and personalisation
* Own online inference – build low-latency, high-reliability serving for customer-facing decisions
* Translate business problems into ML use cases and ship solutions end-to-end
* Raise engineering quality – architecture, testing, observability, automation, CI/CD
* Mentor engineers and act as the senior technical authority across data/ML
* Explore new approaches in ML, MLOps, and GenAI where they can drive product impact
Tech environment
* Languages: Python (strong)
* MLOps: MLflow (or similar), Feature Stores, CI/CD, containerisation, monitoring/alerting
About you
* 7+ years in Machine Learning Engineering / Applied ML / MLOps
* Must have experience with Databricks
* Expert in Python with solid software engineering fundamentals
* Hands-on with cloud platforms and scalable data/ML tooling
* Experience with real-time model serving and high-volume systems
* Deep understanding of MLOps, CI/CD, monitoring, retraining workflows
* Comfortable operating as a principal/lead IC, influencing architecture and standards
* Strong communication and ability to partner across product, engineering, risk, and ops
* FinTech, lending, or transactional environments a plus (not mandatory)
Why this is a standout opportunity
* Huge scope to define a flagship ML capability from the ground up
* High ownership: your work directly impacts core customer journeys
* Modern tech stack and strong investment in ML-led product innovation
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