Principal Machine Learning Engineer

Snr ML Engineer w/ MLOps –
Clinical Data & Women's Health

Location:
Hybrid 2 days in-office min - Melbourne CBD fringe

Type:
Full-time

The Opportunity

We are seeking an experienced Machine Learning Engineer (MLE) with strong MLOps skills to join a high-calibre team at the forefront of healthcare innovation (IVF - AI, Genomics, Precision Medicine). This is a strategic opportunity to push the boundaries in women's healthcare, building and operationalising a program that redefines care pathways and unlocks new models of care for women across their fertility journey and beyond.

As a key member of our growing team of 15+ world-class researchers, clinicians, DS and AI product engineers and product commercialisation execs. This is a deeply hands-on role where you will take ideas from clinical prototype to production, working directly with leadership to deliver scalable, science-led outcomes powered by genomic and biological data.

If you are a high-impact technical engineer who thrives in fast-moving, cross-functional environments and you want your work to make a real-world difference, this is the role

Your Impact & Responsibilities

• Design, build, and maintain production-grade ML pipelines and model serving endpoints in a cloud-native environment.
• Deploy and manage real-time inference services that power our clinical and research applications.
• Automate model training, deployment, monitoring, and rollback processes to ensure reliability and scalability.
• Optimise data and ML workflows for performance, cost efficiency, and compliance with healthcare regulatory standards (e.g., SaMD).
• Implement comprehensive monitoring, logging, and alerting for ML endpoints to guarantee uptime and traceability.
• Collaborate with clinicians, researchers, and engineers to define API contracts and make model predictions reliably consumable downstream.
• Translate complex ML outputs into production-ready services and documentation for integration into clinical systems.
• Contribute to shaping our MLOps strategy and best practices as we scale.

Key Skills & Experience

We are looking for a candidate with a strong foundation in MLOps and Data Engineering, demonstrated by:

• Extensive Programming Skills: 3-5+ years of professional experience with Python (including PySpark) and SQL to build and manage large-scale data workflows.
• Cloud & MLOps Expertise: 3+ years of experience in cloud-native environments (e.g., AWS, GCP, Azure) and 2-3+ years of hands-on experience with ML lifecycle tools (ideally Databricks, MLflow, Kubeflow, SageMaker, or Vertex AI).
• Production Deployment: 2-3+ years of experience deploying, operating, and scaling real-time ML inference services and setting up robust CI/CD workflows for ML environments.
• API & System Design: A strong understanding of REST API design, schema validation, and error handling, with at least 2 years of relevant experience.
• Monitoring & Optimisation: Proven ability to implement monitoring and logging (e.g., Prometheus, Grafana) and a track record of optimising pipelines for performance, reliability, and cost.
• Collaborative Mindset: A passion for working in multidisciplinary teams and bridging the gap between engineering and real-world clinical applications.

Bonus Qualifications (Not Essential):

• Experience designing pipelines and governance practices in regulated or sensitive data contexts (e.g., SaMD, healthcare, finance).
• A Master's degree or PhD in Machine Learning, Artificial Intelligence, Biostatistics, or a related discipline.

Why Join Us?

• Drive Meaningful Impact
• Work with Industry Leaders
• Shape the Future

Flexible Work Environment

• Hybrid model requiring 2 days per week min in the Collingwood office.