Production Systems | Visual Intelligence | Real-World Signal Extraction
We build production-grade systems that extract reliable signal from messy, real-world visual data — systems trusted because they work consistently under real operating conditions, not because they look impressive in demos.
We are looking for a practical, production-minded Computer Vision Engineer who wants to build systems that create measurable operational impact.
This role carries real ownership, fast feedback loops, and the opportunity to work on systems deployed directly into live environments.
The Opportunity
You will work closely with domain experts, researchers, and engineers to build computer vision systems that:
extract actionable signal from video and image dataimprove decision-making through proprietary data generationcreate scalable, trusted operational intelligencecontinuously improve through live feedback loopsturn unstructured visual information into measurable outcomes
You will own systems end-to-end, from problem framing, to ideation, execution and iteration.
What Makes This Role Different
We care about reliable systems, measurable outputs, and quick learning cycles.
You will operate with:
direct access to domain expertshigh autonomytight operational feedback loopsminimal bureaucracy
This is an setting for builders who care more about useful systems than technical perfection.
What We're Looking For
You have already built systems that operate reliably in production environments.




You are comfortable working with:
imperfect or noisy visual dataambiguous problem definitionsrapidly evolving requirementsfast iteration cyclesoperationally critical systems
You move quickly, and are a practical thinker.
You care about the outcomes and are likely stronger at shipping than presenting.
Technical Background
You will probably have strong experience across several of:
computer vision and image/video processingPython and modern ML toolingmodel training, evaluation, and deploymentproduction ML systems and infrastructuremonitoring, observability, and drift detectiondataset creation and labelling workflowsreal-time or streaming data systemsexperiment tracking and reproducibility
Experience working with production-grade data pipelines or operational AI systems is highly valuable.
We care more about judgement, execution speed, and production ownership than academic prestige.
What Success Looks Like
Within the first few months, we would expect:
production-ready CV systems deployed into live workflowsmeasurable improvements in signal quality and reliabilitytighter iteration and feedback cyclesrobust monitoring and retraining processes establishedsystems trusted by operators and stakeholders in production
Success in this role is measured by operational usefulness and system reliability.
Compensation
Compensation is flexible and designed to attract exceptional individuals.
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📌 Lead Computer Vision Engineer (Melbourne)
🏢 seek.com.au
📍 Melbourne