Engineering Manager: Personalization

• 4+ years of experience building and deploying machine learning systems in production

• 1+ year of experience leading engineering teams (people management - experience hiring, mentoring, and developing engineers and understanding that is what you want to do)

• Strong understanding of machine learning fundamentals, hands-on experience with modern ML frameworks and the end-to-end ML lifecycle

• Strong Python skills and experience working with large-scale data systems, with strong software engineering fundamentals, including distributed systems, APIs, and system design

• Experience designing, evaluating, and deploying models that drive measurable business outcomes

• Experience with experimentation methodologies, A/B testing, and metric-driven product development

• Excellent written and verbal communication skills in English

As Engineering Manager of the Personalization team, you will lead the group responsible for the machine learning foundation that powers personalization across Constructor's products, with a particular focus on Search and Browse. The team develops the core intelligence layer that helps us understand shoppers, products, and their interactions, enabling more relevant experiences and measurable business impact for retailers.

This is a highly technical, hands-on leadership role. You will manage Machine Learning Engineers, Backend Engineers, and Data Analysts to develop user representations, embeddings, segmentation systems, offline evaluation frameworks, and experimentation methodologies that drive revenue growth through continuous iteration and A/B testing.

Success in this role is measured by your team's ability to deliver meaningful retailer KPI improvements, scale our personalization capabilities, and advance the state of the art of ecommerce machine learning in production.

The Personalization team sits at the center of Constructor's ML strategy, building capabilities that are leveraged across all products rather than owning a single customer-facing feature

• Lead and grow a cross-functional team of Machine Learning Engineers, Backend Engineers, and Data Analysts

• Guide research and experimentation efforts, balancing long-term innovation with near-term business impact

• Partner with Product and Engineering leaders to identify opportunities for increasing customer revenue and conversion through personalization

• Act as a player-coach: contribute to architecture, model design, technical reviews, and key implementation decisions