Senior Machine Learning Engineer : Personalization (Remote)

The Personalization team, within the Machine Learning Chapter and Engineering Department, plays a central role in implementing algorithms that utilize personalization signals to optimize for business KPIs like revenue & conversions. We focus on metrics over features, arming our search, browse, and recommendation products with powerful personalization capabilities that bring value to customers in the way they care the most about.

The team consists of several engineers owning & collaborating on multiple projects. As a member of the Personalization team, you will use world-class analytical, engineering and machine learning techniques on big data to shape and scale our personalization algorithms.

Challenges you will tackle

• Build and deploy robust ML systems / algorithms for personalized product discovery experiences
• Drive research initiatives to ensure the team adopts an innovative approach and integrates state-of-the-art techniques.
• Conceptualize and implement new personalization features and products that meet customer needs, drive revenue growth and enhance other business KPIs
• Collaborate in architectural discussions and optimize the system architecture of the Personalization domain.

• Data-Driven and Customer-Focused: Able to translate insights into data-driven hypotheses and engineering solutions that drive business value. Passionate about experimentation, and using customer feedback to inform decisions and drive results.
• Personalization Experience: Background in personalized recommendation systems, ranking models, or search relevance.
• ML Expertise: Proficient in machine learning with hands-on experience in applying modern personalized architectures.
• Proven Ability to Deliver: Experience in building and deploying production ML systems, and using A/B testing to validate their value.
• Programming Languages and Tools: Excellent Python skills, experience with at least one DL framework, proficient in SQL, and comfortable with big data tools (Spark, Presto/Athena, Hive).
• ML Workflow Management: Experience with data pipeline orchestration (e.g., Airflow) for automating ML workflows.
• Effective Communication: Clearly explains complex technical concepts and data insights to both technical and non-technical audiences.