About

Every day our ingestion system receives more than 100 million events from our users.

As a Machine Learning Engineer, your mission is to elevate Retail with AI-Driven Solutions.

The final goals are to transform the retail strategy with our AI-powered platform, designed to curate the finest deals and streamline product discovery for consumers, and empower global marketing efforts with automation and personalised content that resonates with shoppers.

Since you will be working in a highly collaborative environment, you will cooperate with Data and Software Engineers, Data Scientists and Product Managers.

Your mission

Your main responsibilities are linked to: Information Extraction and Enrichment from unstructured content, Data Platform activities (e.g., ETLs, ELTs), Multi Modal Extreme classification, Semantic Similarity in a Multi Modal setting and Recommendation systems. Here some examples:

• Autonomy to work on data pipelines, writing SQL queries; processing data using Pandas, Polars or Spark. Following good MLOps practices to develop and implement AI models in production environments, serving +100k predictions per day;
• Design a novel solution, from the problem to the final deployment stage; Develop AI models to process organic unstructured content such as offer images or text to extract valuable data for our users or clients;
• Manage, work on and improve the extensive Python codebases, following the company’s internal code style and guidelines;
• Keep up with the pace in the AI space! We tackle hard problems, we make tangible impact, so every advancement in the field, whether it is a new architecture or an off-the-shelf open source model, can bring us and our clients a ton of value! We value knowledge and curiosity, a lot.

This is what you bring along

• Coding: Proficient Python knowledge; Proven experience with Pytorch, Pandas, FastAPI/Flask, HuggingFace models ecosystem. Software Engineering skills are as important as the AI-related ones;
• MLOps : Fully autonomous in building the pipelines (data, modelling, deployment) to bring the AI solutions from experiments to production;
• Natural Language Processing (NLP) and GenerativeAI: Advanced knowledge of neural network architectures such as Transformers, GPT and proven experience on developing projects in this area (text classification, Named entity recognition, caption generation, unstructured data extraction, etc);
• Image and content: Solid Knowledge of neural architectures in the Computer Vision field, such as YOLO; Multi Modal Language Models or Vision Language Models. Proven experience on developing projects in this area (image classification, image-text search, image understanding, image captioning, etc);
• Search: OpenSearch or ElasticSearch knowledge is a nice to have;
• Recommending Systems: Good to have some proven experience (previous projects, academic works..) in building recommendation engines such as collaborative, content-based, knowledge based filtering based on custom deep learning models or available libraries like Surprise, LightFM, Implicit etc..

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