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Machine Learning Engineer

Il y a 2 mois


Paris, Île-de-France Ipsos Temps plein
About the Role

We are seeking a highly skilled Machine Learning Engineer to join our team at Ipsos Synthesio. As a Machine Learning Engineer, you will be responsible for developing and implementing machine learning models using state-of-the-art algorithms in areas such as Natural Language Processing (NLP), computer vision, and generative AI.

Key Responsibilities
  • Develop and implement machine learning models using state-of-the-art algorithms in areas such as NLP, computer vision, and generative AI.
  • Improve existing machine learning systems, e.g. automated sentiment & emotion analysis, named entity recognition, logo detection, scene recognition, topic modeling, etc.
  • Perform statistical analysis and tune using test results.
  • Extend the MLOps pipelines to improve data and model delivery speed and quality.
Requirements
  • Proven experience as a Machine Learning Engineer or similar role especially in NLP.
  • Strong background in computer science and software engineering: data structures, algorithms, object-oriented programming, code writing, and reviewing.
  • Deep knowledge of machine learning algorithms (supervised, unsupervised, deep learning, transformers,...).
  • Rigor and strong appetite for software quality.
  • Interested in manipulating dozens of microservices in data processing pipelines handling billions of documents.
  • Professional in English and fluent in French.
  • Real Big Data experience with more than 70 million documents ingested every day, and a total of around 100 billion unique documents in storage.
About Ipsos Synthesio

Ipsos Synthesio is a leading marketing SAAS company. Our technology powered with cutting-edge ML models allows global brands to make business decisions based on online consumer feedback. Synthesio centralizes public data from many providers, such as posts on social networks, press articles, forums, search data, and more.

What We Offer
  • Complementary health insurance.
  • Ownership: Each team defines its own schedule of deliveries and methodology (from scrum to Kanban) and owns their projects from design to production.
  • Test And Learn: We are not afraid to fail but we are afraid of not trying.