Machine Learning Engineer

Vestiaire Collective

Foundational Machine Learning Engineer at Vestiaire Collective enhancing MLOps infrastructure and deploying AI solutions for luxurious product authentication. Transform the fashion industry focusing on sustainability.

Posted 6/4/2026full-timeBerlin • 🇩🇪 GermanyMid-LevelSeniorWebsite

Tech Stack

Tools & technologies

AirflowAWSAzureDynamoDBEC2Google Cloud PlatformOpen SourcePyTorchRayRedisTensorflow

About the role

Key responsibilities & impact

Partner closely with the Operations squads and Data Scientists to accelerate ML and RAG prototypes into resilient, production-ready code.
Directly integrate with the team to deploy, optimize, and scale heavy-width CV and VLM models focused on fraud detection and luxury product authentication.
Lead the end-to-end foundational groundwork of our ML lifecycle by designing robust systems for Data & Feature Management, Model Tracking & Registry, and Model Serving & Monitoring.
Scale infrastructure by automating continuous retraining pipelines that handle diverse deployment cadences (from daily fraud detection to weekly recommendations).
Design resilient multi-model architectures, and critically evaluate the technical overhead and TCO of our in-house tools against enterprise-grade platforms to ensure long-term resilience.
Act as a pioneer and cornerstone hire for the ML engineering discipline at Vestiaire Collective, setting the technical standards to help scale the AI/ML organization.
Transition into a centralized foundational role, moving beyond single-squad operations to mentor the team and provide horizontal ML infrastructure support to multiple domains, including Search, Discovery, Pricing, Marketing, and Data Platforms.

Requirements

What you’ll need

5-8+ years of hands-on experience in Machine Learning Engineering, specifically focused on building and scaling MLOps infrastructure and productionizing ML systems.
Proven expertise in deploying low-latency, high-throughput ML inference services (using FastAPI, TorchServe, Triton Inference Server, or Ray Serve) across both classical lightweight and heavy-width ML models (PyTorch/TensorFlow).
Strong preference for AWS (EKS, EC2, SageMaker) / Snowflake and Open Source ecosystems over GCP/Azure.
Deep experience building automated, continuous model retraining pipelines to handle concept drift (ranging from daily to weekly cycles).
You have orchestrated decoupled, multi-model AI architectures using tools like Airflow, Kubeflow, or Metaflow, and possess strong expertise in model registry and tracking tools like MLflow or Weights & Biases.
Hands-on experience evaluating, building, or extensively leveraging online (Redis, DynamoDB) and offline (Snowflake, S3) Feature Stores in a production environment.
Familiarity with frameworks like Feast or custom dbt-based pipelines is highly valued.
You are an analytical builder who thinks long-term.
Strong cross-functional communication skills. You excel at translating complex ML prototypes into highly scalable production code backed by strict version control, rigorous testing, and CI/CD best practices, seamlessly connecting data science innovation with backend engineering execution.

Benefits

Comp & perks

A meaningful job with an impact on the way people consume fashion and promote sustainability
The opportunity to do career-defining work in a fast-growing French-born scale up
The possibility to work as part of a globally diverse team with more than 50 nationalities
Two days to help Project - reinforcing your activist journey and volunteer for an association
Significant investment in your learning and growth
Competitive compensation and benefits package (i.e 28 days of paid time off)

ATS Keywords

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Hard Skills & Tools

Machine Learning EngineeringMLOps infrastructureML inference servicesFastAPITorchServeTriton Inference ServerRay ServePyTorchTensorFlowautomated model retraining pipelines

Soft Skills

cross-functional communicationanalytical thinkingmentoringlong-term planning