Machine Learning Engineer

France·ParisPermanent contract

Machine Learning EngineerData

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Quick Summary

Key Responsibilities

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,

Technical Tools

Machine Learning EngineerData

Vestiaire Collective is the leading global platform for desirable pre-loved fashion and a pioneer in transforming how people consume fashion.

Our mission is simple: make circular fashion the norm, not the exception.
Through technology, expertise, and a highly engaged global community, we enable millions of people to buy and sell fashion in a more sustainable way.

Founded in Paris in 2009, Vestiaire Collective is now a globally scaled marketplace with offices in Paris, London, Berlin, New York, Singapore, and Ho Chi Minh City, and logistics hubs across Europe, Asia, and the US.

Today, we are a team of around 600 people from over 50 nationalities, united by a shared ambition: to drive meaningful change in the fashion industry.

Our values, Activism, Transparency, Dedication, Greatness, and Collective, shape how we build, collaborate, and grow every day.

About the Role

~1 min read

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.