Face comparison has been the prominent biometric technique for identity authentication and has been widely used in many areas, such as financial services, public security, or whenever knowing who’s behind the camera is required !

Face comparison techniques have been a long-standing research topic in the CVPR community. However, there are still many exciting challenges to address when building face recognition systems that work not only in research papers but in the real world ! These can range from supporting a wide variety of camera qualities and devices, detecting frauds that are becoming ever more professional, to fighting bias for a more inclusive digital world.

During this internship, you will focus on:

• Implementing state-of-the-art deep learning algorithms, basing yourself on extensive research of the existing literature and your knowledge of machine learning.
• Improving current face comparison pipeline, from face detection, face quality assessment to face matching.
• Developing techniques that mitigate bias to help Unissey building a fairer AI that works equally well for everyone.

• Pursuing an MSc / MEng degree in computer science, applied mathematics or any related technical field.
• Prior experience in building and training deep learning algorithms.
• Good theoretical knowledge in computer vision, neural networks, and other DL techniques.
• Excellent coding skills in python (experience with pytorch is a plus).
• Great sense of initiative, autonomy and scientific rigour.
• Curiosity and a strong interest for DL/ML topics and the latest scientific breakthrough.
• Proficiency in English (ability to quickly read and understand research papers).
• Prior experience with Git and collaborative tools is a plus.
• Personal side projects or Kaggle competitions are a plus.

Details :

• Duration : 6 months.
• Salary : according to profile and past experiences.
• Location : 4 rue du Caire, 75002 Paris.
• Start date : March 2022.

Quick screening call.

Technical test, to be completed at home.

Final interview, including a technical part, and a meeting with different members of the R&D team to evaluate your motivation / cultural fit.