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DevOps Engineer / V&V Software Engineer

Summary

Builds and maintains testing pipelines for real-time computer vision systems, focusing on data quality, model accuracy, and performance validation in high-stakes environments.

DevOps / V&V Software Engineer (Computer Vision)

Sydney CBD | Full-time

There’s a class of machine intelligence that never gets talked about.

It understands reality - in motion, in real-time, in environments where failure isn’t an option.

Our client is a small, well-funded team is building exactly that.

They’re looking for a DevOps / Software Engineer who understands that testing machine learning systems isn’t just about pipelines — it’s about truth, accuracy, and trust at scale.

What you’ll be doing

  • Designing and evolving testing and validation environments for complex vision systems
  • Building pipelines that go beyond CI/CD, thinking in terms of data quality, drift, and model behaviour
  • Working with engineers and researchers to stress-test models against reality
  • Tracking subtle shifts in performance, accuracy, latency, consistency, and understanding why they change
  • Bringing structure to systems that are constantly evolving

This is not generic DevOps.

You’ll be operating at the intersection of infrastructure, data, and perception.

What they’re looking for

  • Strong DevOps foundation (automation, CI/CD, containerisation, testing frameworks)
  • Experience working with ML / data / vision systems, or curiosity to go deep
  • Someone who notices small anomalies and asks hard questions
  • Comfort working in fast-moving, research-heavy environments
  • A mindset that leans toward systems thinking over tooling

Why this role is different

  • You’re not supporting software teams; you’re shaping how intelligence is validated
  • The systems you’ll work on interact with the real world, not just users
  • The problems don’t have clean documentation - only signals, outputs, and edge cases

If you’re the kind of engineer who wants to understand how machines actually perceive the world, not just deploy models, this is worth a conversation.

See also

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