DiGEiZ's footfall data offers endless business applications (e.g. merchandising mix, audience segmentation, drive-to-store strategies, media campaign analysis). With 25 shopping malls deployed in Europe over the last 2 years, in the R&D team, we are building the AI powering the DiGEiZ's solution. As a Deep Learning Engineer, you will be working on developing, testing, maintaining, and enhancing our computer vision algorithms.
We are opening a Deep Learning Engineer position to scale our GDPR-compliant clothing reidentification in the 25 malls across Europe and the one coming in the US and Middle East. We are seeking candidates with advanced analytics skills and an academic background in a field linked to applied mathematics, statistics, machine learning, or other related fields. Your missions:
Improve our detection and re-identification models by contributing to enhancing data collection, neural network architectures and training paradigms, pre/post-processing, and performance evaluation.
Provide a bibliography on relevant computer vision projects and implement prototypes based on promising papers.
Facilitate the deployment of R&D prototypes by closely collaborating with the Embedded team.
Master's in deep learning, computer vision, machine learning, or equivalent experience (or PhD).
2 years of experience with machine learning algorithms and tools (Ph.D. considered equivalent to professional experience).
Excellent knowledge in deep learning fields, in particular computer vision, such as object detection, feature extraction and image classification.
Hands-on experience with popular deep learning frameworks (e.g. Pytorch, TensorFlow).
Proficiency with data science and image processing Python libraries (OpenCV, PIL, SciPy, Scikit-Learn, Pandas).
Practical understanding of the mathematics behind modern machine learning, linear algebra, and statistics.
Ability to write high-quality Python code and review code developed by team members.
Team-player willing to build a leading company
Reliable, gets things done
Ambitious
Phone call with CTO
Technical test
Lunch with R&D team
