Full Stack Databricks Engineer

1 week ago

Melbourne, Victoria, Australia Saltworth Full time $120,000 - $180,000 per year

Full Stack Databricks Engineer / MLOps

Location:
Hybrid / Melbourne CBD (onsite 2–3 days per week)

Type:
Full-time

Check, check check Please ONLY apply if:

  • You are a competent Databricks user with demonstrable work experience
  • You can physically work onsite in Collingwood 2–3 days per week
  • You have a commercial track record working on the execution side of product development (this is not a research-only role)

So far so good? Great, you're in for a treat
This is a hands-on, high-impact role at the forefront of women's healthcare innovation. You'll build and operationalise pipelines that power AI-driven tools, reshape care pathways, and unlock new models of care across the fertility journey and beyond.

Skills and Experience

  • Databricks, Lakehouse, MLOps tools, SQL, Python (PySpark), AWS (S3, dev tools)
  • Scalable, secure, high-performance data pipelines; automated ML deployment and monitoring; productionised AI/ML solutions
  • Performance tuning, workflow monitoring, cost optimisation, data warehousing, product delivery with compliance focus
  • Comfortable in multi-disciplinary teams, fast-paced environments, with a passion for improving clinical outcomes

Backed by a global healthcare group,
you'll join a high-calibre, start-up team environment of scientists, researchers, clinicians, engineers, and commercial leads. This role sits within the production execution side of the delivery engine, turning research insights into production-ready solutions that are clinically and commercially deployable. Regulatory compliance is key, and you'll embed it from the ground up.

What's on Offer

  • Work across clinical, tech, and commercial domains
  • Shape a science-led health venture within a global healthcare group
  • Flexible hybrid arrangements (2–3 days in Melbourne CBD)
  • Make a real impact in women's health innovation

Key Responsibilities

  • Build and maintain production-grade data and ML pipelines in an AWS cloud-native environment
  • Transform research datasets into secure, scalable, high-quality pipelines
  • Support data scientists with clean, accurate data for predictive modelling
  • Productise validated models into deployable products
  • Build CI/CD pipelines for automation, monitoring, and optimisation
  • Automate ML model training, deployment, and monitoring at scale with strong governance
  • Embed regulatory compliance (SaMD standards) to ensure clinical readiness
  • Collaborate across research, clinical, tech, and commercial teams
  • Contribute to women's health innovation locally and globally

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