Deep Learning Engineer at Veesion

Paris · Full Time

Veesion was founded by Polytechnique and HEC alumni.

The startup develops a technology that performs live gesture recognition in video content. The potential applications are diverse and our first focus is the retail industry.

Our algorithm automatically detects gestures associated with shoplifting in supermarkets.

We built a video database and negotiated paid tests with three leading French food retailers starting in a few months.

Joining Veesion is the opportunity to contribute to the development of deep learning applied to video content, one of the hottest field of AI.

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Job Description


The mission mainly consists in improving the accuracy of the algorithm we are developing on a proprietary video dataset. Tasks will include:

-Tracking down and implementing state-of-the-art video recognition deep learning architectures [1-2-3-4]
-Implementing a light optical flow based action recognition model [6]
-Designing intermediary descriptors [4-5]
-Designing an unsupervised approach to the problem [7]
-Mastering general data preprocessing techniques [8] and temporal data augmentation
-Implementing an efficient pruning for all neural networks used
-Improving the multithreading and multiprocessing training and inference pipelines
-Collaborating with the Integration team and the Product Development team
-Assisting the CTO in tech related appointments and collaboration with labs
-Explore new tasks related to the algorithm arising from tech watch


– Proven expertise in Deep Learning, especially in computer vision (video related tasks are a plus)
– Interest in research and ability to extract useful information from scientific papers
– Solid grasp of at least one of the following deep learning frameworks : Pytorch, Tensorflow, CNTK, Caffe
– Understanding of convolution and famous related architectures (resnext, I3D, RPN, two-stream networks)
– Analytical mind, ability to take a step back and see the big picture
– Problem-solving aptitude
– Master / PhD in Deep Learning or relevant fields.