Job Specifications
About
ShareID delivers real-time, secure authentication using official ID documents and a simple smile. Our AI-powered solution verifies IDs from over 120 countries with 99.9% accuracy, confirms document ownership, and ensures user liveness—without storing personal data. With our patented technology, users get a reusable digital identity and ongoing access to verifiable credentials.
Our mission is to transform authentication by making it seamless, trustworthy, and user-friendly.
Founded at Station F by Sara, a financial engineer with 9+ years in regulatory risk, and Sawsen, a PhD in computer vision with experience at Cisco’s Innovation Lab, ShareID combines deep expertise in security, AI, and digital identity.
Job Description
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.
You Will
Design and implement advanced computer vision models, with a focus on:
identity document analysis,
forgery / tampering detection,
face authentication and liveness,
deepfake / spoofing attack detection,
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.
Preferred Experience
Job Requirements Translation
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);
Biometrics / face recognition / liveness;
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.
Recruitment Process
Nice-to-Have
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).
Additional Information
Contract Type: Full-Time
Start Date: 05 January 2026
Location: Paris
Education Level: Master's Degree
Experience: > 4 years
Occasional remote authorized