MLOps Engineer

Wallapop is a Barcelona based scale-up driven by the purpose to empower people to embrace a more conscious and human way of consumption. We believe in a world where collaborative economy is mainstream. This is what drives us. 💫

Wallapop operates in Spain, Italy and Portugal, offering a catalogue of several hundreds of millions of products and services. Powered by technical innovation and continuous improvement, we bring together the scale & trust of classifieds with the marketplace’s convenience & reach. 🌱 Our mission is to enable a connected trade ecosystem, making 2nd-hand the norm through smart use of technology.

The Challenge 🧩

Wallapop generates billions of data points daily. With a mature data infrastructure already in place, our Data Science and Machine Learning area is gaining significant momentum. As we scale, we face the exciting challenge of taking our ML Platform to the next level to support complex solutions in Personalization, Search, Trust & Safety, and Logistics. As an ML Engineer (MLOps), you will contribute to the evolution of our ML Platform and MLOps practice. You will partner with Data Scientists, Data Engineers, and DevOps to collaborate on a vision that balances innovation with reliability, ensuring our models scale efficiently to serve millions of users.

What You Will Do 👇

• Iterate and maintain Wallapop’s ML Platform, identifying opportunities to improve speed, reliability, and maintainability. You will help execute the long-term vision and roadmap for MLOps.

• Work hand-in-hand with Data Scientists to support their efforts, ensuring they have the tooling to develop, deploy, and monitor scalable models efficiently.

• Adopt and promote engineering best practices (coding standards, testing, CI/CD) within the ML domain.

• Partner with Data Engineering and DevOps to align ML development with company-wide infrastructure and data governance standards.

• Investigate and integrate new frameworks and tools (e.g., for LLMs or real-time inference) to keep our stack modern and effective.

What We’re Looking For 🔎

• Solid experience contributing to and maintaining production-ready ML platforms and pipelines. You understand the lifecycle from experimentation to monitoring.

• Solid understanding of AWS components (SageMaker, Lambda, S3) and container orchestration with Kubernetes.

• Strong software engineering background with proficiency in , Git, and CI/CD workflows. You write robust, testable code.