DNSE

Overview of job

• Design, train, evaluate, and deploy computer vision models for face matching (selfie ↔ document / reference image) and face liveness detection / anti-spoofing (detecting attacks from photos, replay, masks, deepfake, etc.).
• Optimize models for on-device inference (mobile, edge) — focus on resource constraints (latency, memory, compute).
• Build data pipelines: image/video collection, preprocessing, augmentation, alignment, quality checks, normalization.
• Monitor model performance in production: metrics like FAR / FRR, ROC / AUC, error cases under challenging conditions (low light, extreme pose, blur).
• Collaborate with mobile / SDK / backend teams to integrate models securely into client applications.
• Research state-of-the-art techniques in face recognition, spoof detection, domain generalization, and apply to real-world constraints.
• Continuously iterate and improve based on real-world feedback, edge cases, and adversarial examples.

• Competitive salary + performance bonus & benefits
• Full equipment support (e.g. dev machine, mobile devices for testing)
• Insurance & social benefits as per law
• Opportunities for learning, research, attending conferences, professional growth
• Collaborative, fast-paced, product-driven environment