Computer Vision Engineer

UUG.AI turns camera streams into useful, real-time signals for operations teams. As a Computer Vision Engineer, you work across the model lifecycle: from framing a problem and preparing data to evaluating, deploying and observing models in production.

Your responsibilities:

• Build and improve detection, segmentation, classification and multi-object tracking pipelines.

• Define datasets, evaluation sets and metrics reflecting real customer operating conditions.

• Optimise inference for edge devices and GPU clusters, balancing accuracy, latency and cost.

• Package and serve models through our inference stack, including NVIDIA Triton where appropriate.

• Investigate failures using recordings, telemetry and user feedback.

• Collaborate with platform, workflow and frontend engineers to make model output actionable.

This is a full-time engineering role, with hybrid collaboration from Ghent, Belgium.

Hands-on experience building computer vision systems with Python and PyTorch or a comparable framework. Understanding of model evaluation and representative test sets. Comfortable with video, OpenCV, image processing and production-inference trade-offs. Able to work with tests, containers, APIs, profiling and operational debugging. Clear communication and ownership with support from the team.

Helpful, not all required: YOLO, tracking or segmentation; Triton, CUDA or TensorRT; MLOps, model monitoring and dataset versioning; camera protocols and edge computing; Go or C . Relevant skills and practical experience matter; no mandatory diploma or fixed number of years.

Full-time position with hybrid collaboration from Ghent. Work on production computer vision for varied environments, constrained edge hardware and GPU-backed inference. Collaborate with product and engineering colleagues and grow ownership from problem definition through deployment, monitoring and iteration.

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