所在地: ILE DE FRANCE (except Paris)
Machine Learning and Deep Learning Scientist.
For more than a century, L’Oréal has devoted itself solely to one business: beauty. It is a business rich in meaning, as it enables all individuals to express their personalities, gain self-confidence and open up to others. From make-up to hair color, from hair care to skincare and fragrances, our products magnify, beautify, soothe, moisturize, strengthen, rejuvenate... Regardless of a person’s age, origin or personality, above all and before all, they enable her or him to care for what is certainly most precious to each man and each woman – and most fragile: the face, the skin, the body… in a word: caring for oneself.
Our research budget reached €794 million in 2015, which represents 3.1% of turnover.
In the last 10 years, our Research budget has increased more rapidly than the Group's sales.
More than 490 patents were registered in 2015. Our workforce consists of 3800 employees, representing about sixty nationalities and thirty different scientific backgrounds (chemistry, physics, biology, dermatology, optics, rheology, IT, statistics, analysis, etc.).
As a scientist specialized in machine learning and deep learning, you will join our Tech Lab to reinvent our Research & Innovation and the world of beauty thanks to artificial intelligence. As a member of the AI Research team, composed of 10 scientists specialized in the fields of Machine Learning, Deep Learning and Computer Vision, you will design and implement predictive models for computer vision. The role will involve collaboration with our optics experts team, in charge of devices design and data acquisition. The project consists in understanding deeply hair pictures, in the process of designing an advanced hair diagnosis tool. This includes analyzing, structuring, and extracting features from such pictures within accuracy and computational time constraints. You will benefit from our bleeding edge computing resources, with Linux servers and multiples GPUs.
· Develop, implement and industrialize artificial intelligence models in response to our innovation issues, with regards of real-world constraints and objectives,
· Find, extract, retrieve, organize and structure data, coming from heterogeneous sources,
· Develop and evaluate models in multiple environments: cloud server, GPU, embedded on device,
· Push your models into production, in coordination with IT team,
· Working in our environment: Gitlab, Docker, Linux (Ubuntu/CentOS)
· Communicate in order to contribute to the influence of our activity (internally and externally),
· Publish scientific articles or patents,
· Take part in conferences.
· At least 1-3 years of experience in the field of Machine Learning and/or Computer Vision
· You have a degree (Master, Engineering school, PhD) in one of the following disciplines: statistics, probability, optimization, linear algebra
· You have a broad experience with machine learning, and deep learning in particular (CNN, RNN, LSTM, Auto-Encoder, GAN, etc.). Ideally, you are also experienced in one or more of the following areas: reinforcement learning, computer vision, image processing, picture segmentation
· You have a good command in our Python stack: Scikit-Learn, Keras, PyTorch, Tensor Flow, Numpy, Pandas, etc.
· You can easily combine mathematical modeling and software development.
· You have redeveloped and/or improved standard models.
· You feel confident in working on Linux, including basic shell commands.
· You have an entrepreneurial spirit and drive your projects to their completion.
· You are pragmatic and your choices are determined by the satisfaction of the user’s needs.
· You can manage priorities.
· You speak English well.
Who are you?
· An enthusiastic, curious, creative person, who is always on the lookout for new technologies and methodologies.
· You want to develop and enrich your expertise through teamwork.
· You have already taken part in kaggle or hackathon challenges.
· Your last project is on github. You have taken part in free software projects.
· You like bringing your ideas face to face with a challenging environment.