Computer Vision Engineer

pTrain, optimise, and ship detection, tracking, and classification models that run on edge devices and GPU clusters. /p pUUG.
AI turns camera streams into useful, real-time signals for operations teams. As a Computer Vision Engineer, you will work across the model lifecycle: from framing a problem and preparing data to evaluating, deploying, and observing a model in production. /p pThis is an engineering role for someone who enjoys making computer vision reliable outside a notebook. You will work with recordings from varied environments, constrained edge hardware, GPU-backed inference, and the product teams that turn model output into workflows people can trust. /p pYour impact /p h3What you will work on /h3 ul liBuild and improve detection, segmentation, classification, and multi-object tracking pipelines. /li liDefine datasets, evaluation sets, and metrics that reflect the real operating conditions of customer deployments. /li liOptimise inference for edge devices and GPU clusters without losing sight of accuracy, latency, and cost. /li liPackage and serve models through our production inference stack, including NVIDIA Triton where it fits. /li liInvestigate model failures using recordings, telemetry, and user feedback, then turn findings into measurable improvements. /li liCollaborate with platform, workflow, and frontend engineers so model output is understandable and actionable. /li /ul pAbout you /p h3You may thrive here if /h3 ul liYou have hands-on experience building computer vision systems with Python and PyTorch or a comparable framework. /li liYou understand model evaluation and can explain why a metric or test set represents the problem being solved. /li liYou are comfortable with video, image processing, OpenCV, and the practical trade-offs of production inference. /li liYou can move between experiments and engineering work: tests, containers, APIs, profiling, and operational debugging. /li liYou communicate assumptions and results clearly and are comfortable owning a problem with support from the team. /li /ul pUseful additions /p h3Helpful experience /h3 ul liYOLO-family models, tracking algorithms, or segmentation workloads /li liNVIDIA Triton, CUDA, TensorRT, or GPU performance profiling /liliCamera protocols, streaming media, or edge-computing environments /li liGo or C++ alongside Python /li /ul pHow we work /p pSkills differ by role. These are the behaviours we expect from everyone building UUG.
AI. /p ul li01h3Strong communication /h3pShare context, decisions, and concerns clearly. Ask questions early, listen carefully, and adapt the message to the people involved. /p /li li02h3Disciplined and honest /h3pDo what you say, work with care, and be direct about uncertainty or mistakes. We value evidence and transparency over appearances. /p /li li03h3Team player and owner /h3pHelp the team succeed while taking responsibility for the outcome. Collaborate openly, follow through, and leave the work better than you found it. /p /li /ul pA clear start /p h3What to expect in your first months /h3 pThe exact pace depends on the role and your experience. We use these steps to align on support, ownership, and useful outcomes. /p ul li01h3Learn the operating context /h3pStart by running existing pipelines, reviewing representative recordings, and learning how model output moves through the platform. /p /li li02h3Own a measurable improvement /h3pTake on a bounded model or performance problem, agree on the evaluation criteria, and ship an improvement with the team. /p /li li03h3Grow end-to-end ownership /h3pProgress toward taking a vision capability from problem definition through deployment, monitoring, and iteration. /p /li /ul pHow we hire /p h3A practical conversation, both ways. /h3 pWe want you to understand the work, the team, and our expectations before making a decision. /p