20 publicaciones de empleo destacadas de detection engineer en catalunya (¡Están contratando!)

Mid-Level Computer Vision & 3D Deep Learning Engineer
Crisalix LABS, SLU —Barcelona, Barcelona provincia
- Jornada completa
- Español
- Inglés
- Investigación
Solicitud rápida
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- Trabajo híbrido
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- Horas extra
- Inglés
- Máster
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T-Systems Iberia —Barcelona, Barcelona provincia
- Trabajo híbrido
- Español
- Azure
- Inglés
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dLocal —Barcelona, Barcelona provincia
- Jornada completa
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Internship Programme 2027: Software Engineer (Java)
Revolut —Barcelona, Barcelona provincia
- Jornada completa
- Informática
- Inglés
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Preply —Barcelona, Barcelona provincia
- Jornada completa
- Trabajo híbrido
- Máster
- AWS
- Machine learning
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- Español
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2d
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Revolut —Barcelona, Barcelona provincia
- Jornada completa
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Preply —Barcelona, Barcelona provincia
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- Trabajo híbrido
- DevOps
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4d
Database Reliability Engineer
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- Scripting
- Abono gimnasio
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Solicitud rápida

Quiero recibir la alerta de empleo más reciente de detection engineer en catalunya

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Mid-Level Computer Vision & 3D Deep Learning Engineer

Crisalix LABS, SLU
Barcelona, Barcelona provincia

Solicitud rápida

Detalles del empleo

Jornada completa

Cualificaciones

TensorFlow
Español
Optimización del rendimiento
Visión artificial
Inglés
Investigación
Docker
Deep learning
OpenCV
Escalabilidad
Python
Debugging

Descripción completa del empleo

About the role:

We are looking for a Computer Vision Engineer with a solid background in deep learning and 3D data processing 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 (from statistical shape models to implicit neural representations and texture synthesis). at the intersection of classical 3D geometry and modern neural approaches.

This role is ideal for someone with 2–3 years of hands-on experience who enjoys bridging research and production, and is comfortable designing and training pipelines, evaluating reconstruction quality, and integrating your work into a complex multi-stage system.

Responsibilities

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,..)

Our ideal candidate would have:

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).

We also value very positively:

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.

Solicitud rápida

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Quiero recibir la alerta de empleo más reciente de detection engineer en catalunya

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