Vestiaire Collective
Fashion
Senior/StaffMachineLearningEngineer
Neural analysis suggests this role is
optimal for Senior candidates.
“Senior/Staff Machine Learning Engineer at Vestiaire Collective. Skills: Machine Learning Engineering, MLOps, AI authentication, Computer vision, Search, Recommendation systems. Accelerate ML and RAG prototypes. Integrate with team to deploy models”
What You'll Achieve.
Deploy models for fraud detection; Deploy models for product authentication; Improve trust and safety ecosystem; Deliver high-throughput, low-latency business impact
Industry & Context.
Analytical builder; Troubleshooting
What They're Looking For.
Must Have
5-8+ years Machine Learning Engineering, Deploy low-latency, high-throughput ML inference services, Build automated, continuous model retraining pipelines, Orchestrate decoupled, multi-model AI architectures, Expertise in model registry and tracking tools, Hands-on experience with Feature Stores, Analytical builder mindset, Cross-functional communication skills, Translate ML prototypes into production code, Strict version control, Rigorous testing, CI/CD best practices
Nice to Have
Experience in E-commerce, Single-SKU Marketplaces experience, Search & Recommendation experience, Trust & Safety experience, Counterfeit Detection experience, Experience with Vector Databases, Visual RAG pipelines experience, Deploy Deep Learning VLM models, Optimize models for edge computing, Low-latency inference optimization, Advanced experience with containerization, Advanced experience with Infrastructure as Code, Advanced experience with data transformation workflows, Setup advanced monitoring for model performance, Setup advanced monitoring for concept drift, Setup advanced monitoring for system health
What You'll Do.
Accelerate ML and RAG prototypes
Integrate with team to deploy models
Improve trust and safety ecosystem
Design foundational ML lifecycle systems
Design Data & Feature Management systems
Design Model Tracking & Registry systems
Design Model Serving & Monitoring systems
Scale infrastructure automating retraining pipelines
Handle diverse deployment cadences
Design resilient multi-model architectures
Evaluate technical overhead of tools
Evaluate TCO of tools
Set technical standards for AI/ML organization
Mentor ML engineering team
Provide horizontal ML infrastructure support
How You'll Work.
Team & Collaboration
Partner with Operations squads; Partner with Data Scientists; Bridge Applied Science; Bridge Data Platform; Bridge Backend Engineering; Cross-functional communication
Communication Scope
Cross-functional communication
Process & Methodology
Roadmap planning
Full Job Description
## Description 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 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
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