Lead Personalization Engineer

This is a Full Time Job

Be Part of What's Next
Help shape the next generation of personalized discovery at Hearst. In this hands-on engineering leadership role, you'll architect and build the systems that power smarter content and commerce recommendations-creating experiences that deepen engagement, drive conversion, and build loyalty across our digital ecosystem.

You will work closely with product managers, data, designers, and others to deliver scalable, intelligent recommendation features that surface the right content, commerce products, and offers to the right users at the right time. You will also lead coordination across internal and external engineering resources, and guide technology choices for long-term scalability and innovation.
Key Responsibilities (What You're Doing)
• Architect and develop services for real-time personalization across content feeds, product recommendations, newsletters, and marketing funnels.
• Build scalable APIs and supporting infrastructure to enable dynamic recommendation experiences across web, mobile, and email.
• Design end-to-end implementations for recommendation features, from data ingestion through ranking logic to delivery.
• Ensure high standards for performance, security, observability, and fault tolerance across production systems.
• Partner with Product, Data, and UX to define technical requirements that balance personalization sophistication with performance and privacy.
• Partner with ML/GenAI teams to evaluate and integrate LLM-enabled capabilities (e.g., semantic search, affinity prediction, ranking, and summarization) where they improve personalization outcomes.
• Lead experimentation on AI-powered recommendation enhancements (e.g., hybrid LLM and collaborative filtering approaches).
• Provide hands-on mentorship and technical leadership to a blended team of full-time and contract engineers; drive planning, scoping, and prioritization.
• Lead build-vs-buy evaluations for personalization tooling, experimentation platforms, and recommendation infrastructure; integrate third-party solutions as needed.

Qualifications (What We're Looking For)
Must-have
• 10 years in software engineering, with a focus on backend systems, personalization, or e-commerce platforms.
• Hands-on development expertise in Python, React, and building microservices at scale.
• Deep knowledge of recommendation systems: collaborative filtering, ranking algorithms, content-based and hybrid models.
• Ability to lead through influence in cross-functional environments and drive alignment across product, data, design, and engineering stakeholders.
• Demonstrated experience guiding and mentoring engineers, including coordinating work across contractors and/or vendor partners.
• Hybrid requirement: This role is based in New York City with an expectation of 4 days per week in the office.

Preferred
• Experience delivering both content and product recommendation engines in production.
• Experience integrating large language models (LLMs) into consumer-facing personalization or recommendation workflows.
• Familiarity with AI model operations and deployment platforms (e.g., Vertex AI, AWS Bedrock).
• Experience collaborating closely with analytics/experimentation teams to measure impact and iterate on recommendation quality.