Mid-Level Computer Vision & 3D Deep Learning Engineer

We are looking for aComputer Vision Engineerwith a solid background indeep learning and 3D data processingto 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 with23 years of hands-on experiencewho 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.

• 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..)

• 23 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).

• 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.