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
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📌 Senior Computer Vision Engineer (San Cugat del Vallés)
🏢 Innovamat
📍 San Cugat del Vallés