Experteer Overview In this role you will own and advance models that read handwritten student input on digital boards, enabling real-time understanding and feedback in the classroom.

You will shape architecture, input representations, and training methods to handle variable numbers of handwritten objects.

You’ll work with a small team close to product decision-makers to ensure robust, deployable solutions that perform in school environments.

Your work directly influences how students learn math with AI-assisted handwriting interpretation, delivering measurable classroom impact.

Compensaciones / Ventajas• Own the models: architecture, input representation, instance proposal/assignment, training, and iterative improvement at scale• Design and implement changes to address hard cases and ensure trustworthy results• Maintain a fair experimental framework: predefined success criteria, clear trade-offs, and rigorous reporting• Deliver production-ready models: fast on standard school hardware,



with reliable deployment and rollback mechanisms• Collaborate with datasets/evaluation owner to target impactful improvements• Translate research findings into deployable improvements that survive real-world student data• Document experiments and ensure reproducibility, including failed attempts

Responsabilidades• Real depth in multiple-instance detection or instance segmentation• Experience with non-rasterized input (strokes, polylines, trajectories, etc.)• Attention-based architectures over sets and graphs; understanding spatial/relational encoding• Serious experience training deep models in PyTorch (not just using APIs)• Experimental discipline for fair comparisons and noise control• Ability to take models into production: export, latency, determinism, rollback

Requisitos principales•

📌 Senior Computer Vision Engineer (España)
🏢 Innovamat
📍 España