Machine Learning Engineer, Wallapop

• Proven experience building and owning production-ready ML platforms and pipelines. You understand the full lifecycle from experimentation to monitoring
• Deep understanding of AWS components (SageMaker, Lambda, S3) and container orchestration with Kubernetes
• Strong software engineering background with proficiency in Python, Git, and CI/CD workflows. You write robust, testable code
• Experience with real-time ML architectures, leveraging tools like Kafka for low-latency ingestion and inference
• Hands-on experience with vector databases or semantic search infrastructure (e.g., OpenSearch, Vertex AI), including indexing and retrieval tuning
• Familiarity with the broader ML toolkit, such as orchestration/tracking tools (Flyte, MLFlow, Feast) and standard libraries (Pandas, Scikit-learn, TensorFlow/PyTorch)
• Professional proficiency in English and Spanish, with the ability to explain complex technical concepts to diverse stakeholders

Desirable

• As a Senior ML Engineer, you will lead the evolution of our ML Platform and MLOps practice. You will partner with Data Scientists, Data Engineers, and DevOps to shape a vision that balances innovation with reliability, ensuring our models scale efficiently to serve millions of users
• Iterate and maintain Wallapop’s ML Platform, identifying opportunities to improve speed, reliability, and maintainability. You will define 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
• Define 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

• Please, note that all interviews take place remotely over hangouts.**
• Intro Call - run by Talent Acquisition, focus on providing more information about the role and the company as well as going over your experience, motivation, and expectations. This usually takes 45-60 minutes
• Technical Task - you will be assigned a challenge to assess the technical skills required for the role
• Expertise Interview - run by the core team, focusing on the hard skills and the ability to deliver in a given context. This usually takes 60-90 minutes
• Stakeholder Interview - run by the hiring team and relevant stakeholders, focus on the ability to collaborate & deliver in a cross-functional set-up. This usually takes 60 minutes
• Culture-Add Interview - run by culture interviewers, focus on adherence to Wallapop's purpose and business proposition. This usually takes 60 minutes
• Offer - should you be the right candidate, your offer will be discussed over a call with talent acquisition and will then be confirmed in writing