Mid-Level Computer Vision & 3D Deep Learning Engineer

Crisalix
Oña, Spain
2 days ago

Role details

Contract type
Permanent contract
Employment type
Full-time (> 32 hours)
Working hours
Regular working hours
Languages
English, Spanish
Experience level
Intermediate

Job location

Oña, Spain

Tech stack

3D Visualization
Artificial Neural Networks
Computer Vision
Software Debugging
Python
OpenCV
Performance Tuning
TensorFlow
PyTorch
Deep Learning
Machine Learning Operations
Docker

Job description

OverviewMid-Level Computer Vision & 3D Deep Learning Engineer - BarcelonaResponsibilities- Research, 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).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.- Comfortable working in a research codebase with complex multi-stage pipelines.- Fluent or proficient in English (Spanish is a plus).Nice-to-have- Experience with 3D vision techniques (e.G. NeRFs, differentiable rendering, SLAM).- 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.About usCrisalix develops state-of-the-art online 3D visualization solutions used by doctors and patients throughout the patient journey.Our proprietary platform is used by patients, leading medical aesthetic brands and healthcare professionals worldwide.We are a market leader driven by improvements on key medical and business metrics.Contact: if you would like to know more about us, please apply in English or send an email to *#J--Ljbffr

Requirements

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.

  • Comfortable working in a research codebase with complex multi-stage pipelines.

  • Fluent or proficient in English (Spanish is a plus). Nice-to-have- Experience with 3D vision techniques (e.G. NeRFs, differentiable rendering, SLAM).

  • 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.About usCrisalix develops state-of-the-art online 3D visualization solutions used by doctors and patients throughout the patient journey.

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