> Markdown version of [/jobs/ext/2510646-senior-computer-vision-engineer](https://www.wearedevelopers.com/jobs/ext/2510646-senior-computer-vision-engineer). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Senior Computer Vision Engineer - **Company:** Innovamat - **Location:** Sant Cugat del Vallès, Spain - **Contract:** Permanent contract - **Skills:** Computer-Aided Design, Geographic Information Systems, Application Programming Interfaces (APIs), Artificial Neural Networks, Computer Vision, Software Debugging, Vector Graphics, Spatial Data Infrastructures, Pytorch, Deep Learning - **Published:** August 24, 2026 - **Apply:** https://es.indeed.com/viewjob?jk=dcf696cbddd96701 ## About the Role We are looking for depth rather than breadth. The bar is someone who could plausibly take a problem of this kind as far as anyone has taken it., * Real depth in multiple-instance detection or instance segmentation. You have built models that find a variable, unknown number of objects in a single input and attribute each part of that input to exactly one of them - including how predictions get matched to ground truth during training, and what the resulting metrics do and don't tell you. * Experience with non-rasterized input, or a clear grasp of what changes without a pixel grid. Strokes, polylines, trajectories, point clouds, graphs, vector graphics, CAD geometry or sensor traces - anywhere the input is a set of geometric primitives rather than an image. If your instance-detection work has been on pixels, we will want to talk about how you would approach it without them. * Attention-based architectures over sets and graphs, and a feel for how spatial and relational structure gets encoded into a model - including the instinct for when a model simply cannot see something you assumed it could. * Serious experience training deep models in PyTorch - not calling a training API, but owning the loop: schedules, numerical stability, distributed runs, and the debugging of a model that trains happily without getting better. * The experimental discipline to run fair comparisons, resist reading noise as signal, and say plainly when a result is inside the band. * Evidence you can take a model into production - export, latency, determinism, and a rollback path. * The ability to read the literature critically: reproduce what is useful, and tell a genuine advance apart from a benchmark artifact. Nice-to-haves * Online handwriting, sketch, ink or diagram recognition, in any script or notation. * Geometric deep learning, or graph neural networks applied to spatial data. * Vector graphics, CAD, GIS or trajectory modelling in an industrial setting. * Inference optimization and quantization. * Published or open-sourced work we can read. * Spanish or Catalan. ## Description * Take architectural responsibility for the models that read handwriting - how the input is represented, how instances are proposed and assigned, how the models are trained, and how they get better. * Design the changes you believe will move the hard cases, implement them properly, and run them at a scale where the answer is trustworthy. * Keep a fair challenger alive. Whatever we are running is the incumbent, not a commitment, and part of the job is knowing when a different formulation would beat it. Make the gains real ones * Run controlled comparisons with success criteria agreed before the run, so a result means what it appears to mean. * Look past the headline number at which kinds of page improved and which regressed, and judge whether the trade was worth making. * Replicate anything you intend to ship, and report the attempts that went nowhere as clearly as the ones that worked. Get it into classrooms * Take a model through to something that answers fast enough on ordinary school hardware, not only on a training GPU. * Guarantee that what we deploy behaves like what you evaluated, and that we can roll it back the moment it doesn't. * Watch what it does on real student work once it is live, and turn what you see there into the next thing you try. Choose what is worth building next * Work closely with the person who owns our datasets and evaluation, and be specific with them about what would actually change a result. * Judge honestly when the limit is the model, when it is the data, and when the task itself is genuinely ambiguous. ## Related Videos - [Focoos AI: Building the Future of Computer Vision](https://www.wearedevelopers.com/videos/1659-focoos-ai-building-the-future-of-computer-vision) - [Getting Started with Machine Learning](https://www.wearedevelopers.com/videos/260-getting-started-with-machine-learning) - [Geometric deep learning for drug discovery](https://www.wearedevelopers.com/videos/264-geometric-deep-learning-for-drug-discovery) - [Photonic Computing: Programming a New Class of AI Accelerators (incl. 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