About the Role
Lea el resumen de esta oportunidad para comprender qué habilidades, incluidas las habilidades interpersonales relevantes y el dominio de paquetes de software, se requieren.
Mid-Level Computer Vision & 3D Deep Learning Engineer. 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, including statistical shape models, implicit neural representations and texture synthesis.
Responsibilities
Research, prototype, and integrate new deep learning algorithms from recent literature (e.g., 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 (e.g., TorchScript/JIT, Triton Inference Server).
Qualifications
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.
Experience working in a research codebase with complex multi‑stage pipelines.
Fluent or proficient in English (Spanish is a plus).
Preferred Skills
Experience with 3D vision techniques such as NeRFs, differentiable rendering, and SLAM.
Understanding of implicit surface representations (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 for reproducibility and scalability.
Understanding of GPU optimization and performance tuning.
Background in geometry, linear algebra, or graphics.
What we offer
Competitive salary and benefits package.
Structured onboarding, mentoring, performance reviews, and training plans to help advance your career.
Flexible hybrid model: 2–3 days per week in our Barcelona office and the remainder remote.
Opportunities for professional growth within an international environment.
Collaborative culture with team events and a focus on work‑life balance.
Location
Barcelona, Spain (hybrid model allowed). xpzdshu
Equal Employment Opportunity Statement
Crisalix is committed to equality of opportunity for all staff, and applications from all suitably qualified individuals are encouraged, regardless of age, disability, sex, gender reassignment, sexual orientation, pregnancy and maternity, race, religion or belief, and marriage and civil partnerships.
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