About the job

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

As part of the Personalization team, you will help shape the e-commerce experience for hundreds of millions of shoppers worldwide.

You will design, deliver, and maintain high-load, real-time machine learning services that power personalized experiences across products, platforms, and domains.

Your work will focus on understanding user interactions and turning behavioral signals into relevant, impactful recommendations and experiences.

You will research and test personalization approaches, using experimentation and data to validate hypotheses and improve key business metrics.

You will work closely with machine learning and engineering teams, including teams responsible for ranking and search, to build cohesive experiences.

The role combines hands-on applied machine learning with product thinking, stakeholder collaboration, and end-to-end ownership.

You will join an open, cooperative, fully remote environment where autonomy, empathy, and measurable impact are highly valued.

Accountabilities

• Develop and improve personalized experiences across different products, platforms, and e-commerce domains.
• Design, build, ship, and maintain high-load, real-time machine learning services supporting personalization at scale.
• Model user interactions and behavioral signals to better understand shopper preferences and deliver relevant experiences.
• Research and evaluate different personalization and recommendation approaches to identify opportunities for improvement.
• Formulate and validate hypotheses around personalization using data, experimentation, and A/B testing.
• Analyze the impact of personalization initiatives and identify opportunities to improve key business performance indicators.
• Collaborate closely with technical and non-technical stakeholders to develop, refine, and evolve personalization capabilities.
• Work cross-functionally with engineering and machine learning teams, including ranking teams, to deliver integrated solutions.
• Communicate technical findings, recommendations, and business value clearly to stakeholders within and beyond the team.
• Take ownership of problems from investigation and experimentation through implementation and continuous improvement.