Research Engineer​/Deep Learning Engineer; Computer Vision – CDI

Job Title

Senior Member Of The R&D Team

As a senior member of the R&D team, you will design, train, optimize, and deploy deep learning models for ShareID's core products:
Document Verification, Face Authentication, Liveness, and Fraud Detection. You will work on computer vision problems involving images, videos, and temporal sequences in highly adversarial (fraud-prone) environments.

• Design and implement advanced computer vision models, with a focus on:

  • identity document analysis,
  • forgery / tampering detection/ Document spoofing,
  • video-based temporal modeling and tracking.

• Experiment with state-of-the-art architectures:

  • transformer-based models (ViT, DETR-like, SAM, etc.)
  • diffusion / generative models for augmentation or anomaly detection,
  • latency-optimized networks (quantization, pruning, distillation).

• Own end-to-end research cycles:

  • literature review,
  • prototyping and experimentation,
  • evaluation on large-scale datasets,
  • productization with engineering teams.

• Collaborate cross-functionally with Product, Risk, Fraud, and Engineering to bring research ideas into production.

• Contribute to ShareID's scientific culture:

  • present papers, lead knowledge-sharing sessions,
  • guide junior ML engineers and interns,
  • optionally participate in benchmarks or publications.

• Minimum 4 years of experience in deep learning applied to computer vision, with a portion of that experience in a production environment (startup, scale-up, industrial lab, etc.).

• Excellent command of:

  • Python;
  • PyTorch (or equivalent);
  • Large-scale model training (voluminous datasets, data augmentation, rigorous validation).

• Concrete experience in at least one of these areas:

  • Real-time vision (tracking, video detection, high-performance pipeline);
  • Document understanding (document scanning, OCR, document augmentation, QA).

• Solid foundation in:

  • Statistics, optimization, supervised / self-supervised learning;
  • Good reading comprehension of literature (ICCV, CVPR, NeurIPS, etc.).

• Experience working in a product environment:

  • Latency, robustness, hardware resource, security, and privacy constraints.

• Experience in documentary fraud, KYC (Know Your Customer), cybersecurity, or digital identity.

• Knowledge of MLOps: model deployment, monitoring, CI/CD, GPU/CPU serving.

• Participation in public benchmarks or publications (arXiv, workshops, conferences).

• French: professional proficiency (a plus); English: fluent (essential).

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