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Senior/Staff Machine Learning Engineer

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EMP:Technology
Berlin
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Descripción

Vestiaire Collective is the leading global online marketplace for desirable pre-loved fashion.

Our mission is to transform the fashion industry for a more sustainable future by empowering our community to promote the circular fashion movement.

Vestiaire was founded in 2009 and is headquartered in Paris with offices in London, Berlin, New York, Singapore, Ho Chi Minh, and warehouses in Tourcoing (France), Crawley (UK), Hong Kong and New York.

We currently have a diverse global team of 600 employees representing more than 50 nationalities.

Our values are Activism, Transparency, Dedication and Greatness and Collective.

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the Role We are seeking a Foundational Machine Learning Engineer for a high-impact greenfield opportunity to build our MLOps infrastructure from the ground up at Vestiaire Collective

While driving our AI authentication initiatives (deploying multi-model approaches including computer vision for luxury product authentication and counterfeit detection) will be your immediate focus, your long-term mission will be to scale foundational architecture across the entire marketplace.

You will expand our ML capabilities to power broader domains, primarily focusing on search and recommendation systems, with future expansions into dynamic pricing and marketing technologies.

  • Acting as the bridge among Applied Science, Data Platform, and Backend Engineering, you will design robust, decoupled architectures and spearhead the MLOps strategy with our Director of Data, prioritizing system maintainability, engineering hygiene, and the reliable deployment of complex models, ensuring all our ML models across the board deliver high-throughput, low-latency business impact.

What You Will Do Short-Term Impact (First 6 Months): Partner closely with the Operations squads and Data Scientists to accelerate ML and RAG prototypes into resilient, production-ready code.

You will directly integrate with the team to deploy, optimize, and scale heavy-width CV and VLM models focused.

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