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Machine Learning Engineer: Ranking

Pubblicato il 17-10-2026 - Jobgether in Verona

This position is listed on behalf of a partner company, which manages all applications and next steps. Our partner is looking for a Machine Learning Engineer: Ranking & Personalization based in Italy.

Non è sicuro/a delle competenze necessarie per questa possibilità? Legga la descrizione completa qui sotto per avere un quadro completo dei requisiti richiesti.
Join a collaborative, AI-driven environment where machine learning powers smarter e-commerce search, discovery, and shopping experiences. In this role, you will design, build, and maintain high-performance, real-time personalization services that reach hundreds of millions of users worldwide. You will work closely with engineers, data specialists, and business stakeholders to develop innovative solutions that improve product discovery and customer engagement. By leveraging user behavior, experimentation, and advanced machine learning techniques, you will help deliver more relevant shopping experiences. Your work will directly contribute to measurable business outcomes, including improved conversion rates and revenue growth.



This is an opportunity to combine technical expertise, analytical thinking, and a strong sense of ownership while making a meaningful impact in a supportive, remote-first team.
Accountabilities
- Design, develop, deploy, and maintain scalable, real-time machine learning services that power ranking and personalization across e-commerce products, platforms, and industry verticals.
- Enhance personalized shopping experiences by developing and refining recommendation, ranking, and discovery capabilities based on user behavior and preferences.
- Analyze user interactions and behavioral data to build effective models that deliver more relevant and engaging customer experiences.
- Research, evaluate, and implement different personalization approaches to identify solutions that maximize customer value and business performance.
- Formulate and validate hypotheses through experimentation and A/B test

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