We are hiring a Machine Learning Engineer or Senior ML Engineer to join a talented team of Data Scientists and Machine Learning Engineers building, scaling and deploying robust production machine learning products! The core focus is working in small squads to build, deploy and productionise pipelines and solutions that will reach millions of Australian customers.

About the Team:

You will be in a small concentrated ML and Data Science team on a 12 month project, with view to extend. Your work will be impactful and this favours the style of MLE job where you productionise and scale solutions. You will be in a cross-functional squad – taking ownership for engineering from system design, suggesting processes and improvements to data scientists & developers for ML Ops (e.g. scalability, reliability, automation, smoother deployment, etc). There will also be opportunities for you to introduce best practices and build tools or frameworks as part of their growing ML platform, that make life easier for future deployment, scalability and maintenance of ML.

Is this a GenAI/LLM/Research focussed role?

No.

The focus is firmly on commercial work, with real data, deadlines and business constraints. You will not be working heavily on GenAI / Agentic work - think more Recommendation Systems for product & marketing, Next-Best-X models, etc.

Examples of work you will do:

You will join a team delivering predictive models that serve recommended content, products and offers to millions of customers! You might help data science peers to deploy and productionise a models in Spark, Google Cloud Platform, and VertexAI You may be building faster and more reliable ML and Data pipelines using Python/SQL/PubSub. You may be helping set-up Kubernetes or introduce CI/CD practices to others.

What tools do they use?

You will work heavily with: Python, SQL, Google Cloud Platform (BigQuery, PubSub, VertexAI), Kubernetes / GKE / Kubeflow, APIs and much more (think any useful tools around ML Ops for CI/CD and automation).

What skills and experience do you need to apply?!

• A minimum of 3-5 year of professional industry experience in Machine Learning Engineering roles - ideally with data at scale of millions of customers
• Strong Python and SQL experience building data and ml pipelines
• Cloud platform experience - strong preference for GCP, including BigQuery, VertexAI, Pubsub & GKE or other kubernetes exp.
• A passion and evidence of self-learning in related fields such as; Big data, ML, Distributed computing, Data structures, Streaming data, MLOps, GenAI, etc
• Must have full Australian work rights and be open to hybrid work in Sydney – 3 days in the office / week
• Ideally, a background in Software Engineering / Computer Science prior to focussing on MLE work

Attractive Daily Rate or Salary on offer - to be discussed with shortlisted candidates.