Mid-Level Computer Vision & 3D Deep Learning Engineer (España)
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Job description
Crisalix - Redefining Aesthetic VisualizationCrisalix is the world's leading technology company in 3D imaging and aesthetic simulation. Leveraging the latest advancements in Artificial Intelligence, Computer Vision and Augmented Reality, our proprietary technology enables the creation of highly accurate 3D reconstructions of the human body and lifelike simulations of aesthetic procedures.With a global presence across five continents, Crisalix empowers some of the world's most recognized plastic surgeons and clinics to enhance patient trust, visualize treatment outcomes, and deliver exceptional results.Role SummaryWe are looking for a mid-level Computer Vision & 3D Deep Learning Engineer to join our team. You will work on developing and deploying models that understand and reconstruct the visual world, contributing to production-grade pipelines that take multi-view 2D images and produce high-quality 3D reconstructions. This role is ideal for someone with 2-3 years of hands-on experience who enjoys bridging research and production, designing and training pipelines, evaluating reconstruction quality, and integrating work into a complex multi-stage system.ResponsibilitiesResearch, prototype, and integrate new deep learning algorithms from recent literature (NeurIPS, CVPR, ICCV, ECCV) to improve 3D reconstruction quality.Develop and maintain deep learning components for multi-view reconstruction, landmark detection, segmentation, inpainting, and view-consistent shape fitting.Implement and tune custom training pipelines and loss functions, and evaluate their impact on mesh and texture quality.Design and run quantitative evaluation experiments using metrics such as reprojection error, surface-to-surface distance, and perceptual quality scores.Export and deploy trained models for inference (TorchScript/JIT, Triton Inference Server, etc.).Qualifications2-3 years of hands-on experience in computer vision and deep learning research or applied engineering.Solid understanding of camera models, projective geometry, and multi-view geometry (epipolar geometry, camera calibration, reprojection).Experience training and debugging neural networks end-to-end, including custom loss functions, learning rate scheduling, and training stability.Comfortable reading and implementing methods from academic papers.Strong Python skills; proficiency with PyTorch (primary) and/or TensorFlow.Comfortable working in a research codebase with complex multi-stage pipelines.Fluent or proficient in English (Spanish is a plus).Experience with 3D vision techniques (e.g., NeRFs, differentiable rendering, SLAM) is highly valued.Understanding of implicit surface representations: Signed Distance Functions (SDFs), occupancy networks, NeRF/neural radiance fields.Familiarity with classical 3D fitting approaches: statistical shape models (PCA-based), iterative closest point (ICP), mesh deformation.Knowledge of differentiable rendering concepts: ray marching, sphere tracing, volume rendering.Familiarity with libraries such as Open3D, PyTorch3D, or OpenCV.Experience with experiment tracking tools (MLflow, W&B) and reproducible training pipelines.Experience deploying models to production environments, using Docker to ensure reproducibility and scalability.Understanding of GPU optimization and performance tuning.Background in geometry, linear algebra, or graphics.What We OfferWe offer a technically deep role at the frontier of 3D AI, competitive salary and benefits, structured onboarding, mentoring, performance reviews, and training plans. We support your professional growth within an international environment and encourage the exploration of new ideas and architectural changes. We provide a versatile hybrid model: spend 2-3 days per week in our Barcelona office and work remotely the rest of the time, balancing flexibility and collaboration.About CrisalixCrisalix is the world's leading tech company in 3D aesthetic simulation. Its unique technology is based on the most recent advances in Artificial Intelligence, Computer Vision and Augmented Reality, enabling the creation of highly accurate 3D reconstructions of human bodies and the simulation of aesthetic procedures. It serves some of the world's most recognized plastic surgeons and clinics across five continents.#J-*****-Ljbffr
Requirements
2-3 years of hands-on experience in computer vision and deep learning research or applied engineering. Solid understanding of camera models, projective geometry, and multi-view geometry (epipolar geometry, camera calibration, reprojection). Experience training and debugging neural networks end-to-end, including custom loss functions, learning rate scheduling, and training stability. Comfortable reading and implementing methods from academic papers. Strong Python skills; proficiency with PyTorch (primary) and/or TensorFlow. Comfortable working in a research codebase with complex multi-stage pipelines. Fluent or proficient in English (Spanish is a plus). Experience with 3D vision techniques (e.g., NeRFs, differentiable rendering, SLAM) is highly valued. Understanding of implicit surface representations: Signed Distance Functions (SDFs), occupancy networks, NeRF/neural radiance fields. Familiarity with classical 3D fitting approaches: statistical shape models (PCA-based), iterative closest point (ICP), mesh deformation. Knowledge of differentiable rendering concepts: ray marching, sphere tracing, volume rendering. Familiarity with libraries such as Open3D, PyTorch3D, or OpenCV. Experience with experiment tracking tools (MLflow, W&B) and reproducible training pipelines. Experience deploying models to production environments, using Docker to ensure reproducibility and scalability. Understanding of GPU optimization and performance tuning. Background in geometry, linear algebra, or graphics.