Senior Machine Learning Engineer
Location: Chatswood, Sydney – hybrid working, three days per week in the office
Reports to: ML Solution Manager
About the opportunity We are seeking a hands-on Senior Machine Learning Engineer to lead the development and delivery of production-ready machine learning solutions that generate measurable business outcomes.
This role will take ownership of machine learning solutions from initial problem framing and model development through to deployment, monitoring and ongoing improvement. You will work closely with MLOps, AI engineering, actuarial and business teams to ensure solutions are scalable, reliable and aligned with organisational priorities.
In addition to delivering your own solutions, you will help establish modelling standards, mentor junior practitioners and contribute to the continued development of the organisation’s machine learning capability.
Key responsibilities Machine learning development and delivery

- Lead the end-to-end development of machine learning models, from problem definition through to production deployment.
- Design, build and deploy robust, scalable and well-documented machine learning solutions.
- Take ownership of the full model lifecycle, including monitoring, maintenance, retraining and ongoing optimisation.
- Ensure solutions meet agreed business requirements, technical standards and success criteria.

Applied analytics and business solutions
- Develop predictive models and analytical solutions across key business areas.
- Build data pipelines and feature-engineering workflows that support machine learning initiatives.
- Apply and refine modelling approaches, analytical methods and data assets.
- Develop machine learning solutions that support customer growth and retention.
- Build proofs of concept that demonstrate measurable value and support the transition from traditional reporting to machine learning-led insights.

Technical leadership
- Influence and establish machine learning and modelling best practices.
- Mentor junior machine learning practitioners and provide guidance on technical approaches and career development.
- Conduct code reviews and help improve the quality of solutions delivered by the wider team.
- Contribute to reusable components,



technical documentation, coding standards and shared frameworks.

Collaboration and stakeholder engagement
- Work closely with MLOps engineering to streamline model deployment and operationalisation.
- Partner with AI solutions engineers, actuarial teams and other technical specialists on cross-functional initiatives.
- Engage with business stakeholders to understand complex requirements and translate them into practical machine learning solutions.
- Clearly communicate technical concepts, model outcomes and trade-offs to technical and non-technical audiences.

Machine learning foundations and governance
- Contribute to scalable machine learning standards, frameworks and engineering practices.
- Support responsible AI, governance and data privacy initiatives.
- Use and contribute to shared platforms and tools, including feature stores, experiment-tracking platforms and model registries.
- Help shape broader technical strategy and machine learning platform direction.

AI-augmented development
- Integrate AI assistants and development tools into day-to-day engineering workflows.
- Use AI tooling to support code generation, debugging, testing and technical documentation.
- Remain current with emerging AI development tools, trends and engineering practices.

Skills and experience Essential
- At least five years of experience in machine learning, data science or a related technical role.
- Proven experience owning machine learning solutions from ideation through to production deployment and ongoing support.
- Demonstrated experience deploying, monitoring and maintaining production machine learning systems.
- Expert-level Python and SQL skills, with an emphasis on clean, testable and production-ready code.
- Strong experience designing, developing and deploying machine learning models at scale.




- Deep understanding of machine learning theory and practice, including model selection, evaluation, bias, interpretability and performance trade-offs.
- Advanced knowledge of feature engineering, data-leakage prevention and common modelling risks.
- Strong understanding of end-to-end machine learning system design, including data ingestion, deployment, monitoring and retraining.
- Experience with cloud-based machine learning platforms and distributed data-processing environments.
- Strong problem-framing skills, with the ability to translate ambiguous business challenges into machine learning solutions with clear success measures.
- Ability to lead modelling decisions involving algorithm selection, feature design and evaluation strategy.
- Strong stakeholder engagement and communication skills.
- Experience mentoring or supporting early-career machine learning practitioners.
- Tertiary qualifications in computer science, data science, engineering, statistics or a related field, or equivalent practical experience.

Desirable
- Advanced knowledge of MLOps architectures, CI/CD practices and model lifecycle management.
- Experience with feature stores, experiment tracking and model registries such as MLflow.
- Experience using data transformation tools such as dbt.
- Exposure to deep-learning frameworks such as PyTorch or TensorFlow.
- Familiarity with large language models and generative AI systems.
- Experience designing shared machine learning frameworks, libraries or standards.
- Experience taking proof-of-concept solutions through to production.
- Experience making technical decisions under data-quality, risk or time constraints.
- Experience operating machine learning systems in regulated or high-risk environments.
- Experience within insurance, financial services, healthcare or another regulated industry.

The successful candidate You will be a technically strong and commercially minded machine learning skilled who understands that successful models must deliver business value as well as strong technical performance. You will be comfortable taking ownership, solving complex problems, working across technical and business teams and helping others improve their capability.

📌 Senior ML Engineer (Sydney)
🏢 Correlate Resources
📍 Sydney