Job Description

Production Systems | Visual Intelligence | Real-World Signal Extraction

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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.

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We are looking for a practical, production-minded Computer Vision Engineer who wants to build systems that create measurable operational impact.

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This role carries real ownership, fast feedback loops, and the opportunity to work on systems deployed directly into live environments.

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The Opportunity

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You will work closely with domain experts, researchers, and engineers to build computer vision systems that:

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- extract actionable signal from video and image data
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- improve decision-making through proprietary data generation
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- create scalable, trusted operational intelligence
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- continuously improve through live feedback loops
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- turn unstructured visual information into measurable outcomes
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You will own systems end-to-end, from problem framing, to ideation, execution and iteration.

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What Makes This Role Different

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We care about reliable systems, measurable outputs, and fast learning cycles.

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You will operate with:

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- direct access to domain experts
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- high autonomy
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- tight operational feedback loops
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- minimal bureaucracy
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This is an environment for builders who care more about useful systems than technical perfection.

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What We're Looking For

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You have already built systems that operate reliably in production environments.

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You are comfortable working with:

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- imperfect or noisy visual data
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- ambiguous problem definitions
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- rapidly evolving requirements
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- quick iteration cycles
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- operationally critical systems
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You move quickly, and are a practical thinker. You care about the outcomes and are likely stronger at shipping than presenting.

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Technical Background

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You will probably have strong experience across several of:

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- computer vision and image/video processing
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- Python and modern ML tooling
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- model training, evaluation, and deployment
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- production ML systems and infrastructure
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- monitoring, observability, and drift detection
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- dataset creation and labelling workflows
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- real-time or streaming data systems
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- experiment tracking and reproducibility
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Experience working with production-grade data pipelines or operational AI systems is highly valuable.

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We care more about judgement, execution speed, and production ownership than academic prestige.

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What Success Looks Like

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Within the first few months, we would expect:

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- production-ready CV systems deployed into live workflows
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- measurable improvements in signal quality and reliability
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- tighter iteration and feedback cycles
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- robust monitoring and retraining processes established
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- systems trusted by operators and stakeholders in production
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Success in this role is measured by operational usefulness and system reliability.

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Compensation

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Compensation is flexible and designed to attract exceptional individuals.

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📌 Lead Computer Vision Engineer (Victoria)
🏢 seek.com.au
📍 Victoria