Hearst
Principal Software Engineer, Personalization & Recommendation Systems
Hearst, New York, New York, us, 10261
Job Description
Overview (Why This Role?)
We are seeking a Principal Software Engineer to lead the technical development of a next-generation personalization and recommendation platform, powering both content and product discovery experiences across Hearst's digital ecosystem. This is a hands-on engineering leadership role that combines strategic system architecture with deep execution-driving personalized user experiences that increase engagement, conversion, and loyalty.
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.
About Hearst Magazines (Why Us?) Hearst Magazines' portfolio of more than 30 iconic brands in the U.S.-including Cosmopolitan, ELLE, Esquire, Good Housekeeping, Harper's BAZAAR, and Popular Mechanics-inspires, entertains, and builds new and bold experiences for an engaged and growing audience across digital, video, social and print, reaching nearly 130 million readers and site visitors each month. With sophisticated content creation, cutting-edge technology, and industry-leading data capabilities, we make media and products that move people across all platforms. We are a global media company that publishes nearly 200 magazine editions and 175 websites around the world-and together, we are shaping what's next.
Key Responsibilities (What You Are Doing) Hands-On Technical Execution
Architect and develop services for real-time personalization across content feeds, product recommendations, newsletters, and marketing technology funnels. Build robust APIs and infrastructure to support dynamic recommendation use cases on web, mobile, and email. Understand and architect end-to-end implementation of key recommendation features-from data ingestion to ranking logic and delivery. Ensure high standards of performance, security, and fault tolerance in production systems. Collaborate with ML and GenAI teams to evaluate the use of large language models for dynamic content ranking, summaries, or affinity prediction. Drive integration of semantic search and LLM-based personalization where appropriate, ensuring real-time responsiveness and system scalability. Lead experimentation on AI-powered recommendation enhancements (e.g., hybrid LLM and collaborative filtering approaches).
Team Leadership & Coordination
Provide hands-on mentorship and technical direction to a blended team of full-time and contract engineers. Lead planning, scoping, and prioritization of engineering tasks in alignment with cross-functional goals. Ensure code quality, velocity, and accountability across distributed development efforts.
Build-vs-Buy Strategy & Evaluation
Drive build-vs-buy assessments for recommendation infrastructure, experimentation platforms, and personalization tooling. Evaluate and integrate third-party solutions as needed for features such as product recommendation engines and customer data platforms. Contribute to long-term architectural blueprints for personalization across Hearst's brands.
Cross-Functional Collaboration
Partner with Product, Data, and UX to define technical requirements that balance personalization sophistication with performance and privacy. Work closely with ML engineers to integrate trained models into live production flows and iterate based on real-time feedback. Collaborate with analytics and experimentation teams to validate and optimize recommendation impact.
Qualifications (What We're Looking For)
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. Demonstrated ability to lead hybrid teams and manage contract/vendor developers toward shared goals. Preferred experience delivering both content and product recommendation engines in production environments. Preferred experience deploying or integrating large language models (LLMs) into consumer-facing personalization or recommendation flows. Preferred experience working with AI model ops platforms (e.g., Vertex AI, AWS Bedrock) Benefits (What We Offer)
Work with the Best:
Collaborate with top-tier professionals across media, advertising, tech, fashion, lifestyle, and publishing, shaping the future of these dynamic industries. Grow Your Skills:
Unlock your potential with access to innovative training programs, immersive workshops, and exclusive industry events. Work-Life Harmony:
Enjoy the flexibility of hybrid work, empowering you to balance professional success with personal priorities. Foster Connection & Belonging:
Join our Employee Resource Groups and help create a welcoming workplace where everyone feels valued and empowered. Wellness First:
Prioritize your well-being with a comprehensive benefits package that includes medical, dental, and vision insurance from Day 1. Plan for Your Financial Future:
Enjoy competitive financial perks, including a 401(k) plan with a generous company match.
The base salary for this role is between $190,000 - $230,000. The actual base pay offered is dependent upon many factors, such as: transferable skills, work experience, business needs and market demands. The base pay range is subject to change and may be modified in the future.
Hearst Magazines is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, protected veteran status, or any other characteristic protected by law.
Overview (Why This Role?)
We are seeking a Principal Software Engineer to lead the technical development of a next-generation personalization and recommendation platform, powering both content and product discovery experiences across Hearst's digital ecosystem. This is a hands-on engineering leadership role that combines strategic system architecture with deep execution-driving personalized user experiences that increase engagement, conversion, and loyalty.
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.
About Hearst Magazines (Why Us?) Hearst Magazines' portfolio of more than 30 iconic brands in the U.S.-including Cosmopolitan, ELLE, Esquire, Good Housekeeping, Harper's BAZAAR, and Popular Mechanics-inspires, entertains, and builds new and bold experiences for an engaged and growing audience across digital, video, social and print, reaching nearly 130 million readers and site visitors each month. With sophisticated content creation, cutting-edge technology, and industry-leading data capabilities, we make media and products that move people across all platforms. We are a global media company that publishes nearly 200 magazine editions and 175 websites around the world-and together, we are shaping what's next.
Key Responsibilities (What You Are Doing) Hands-On Technical Execution
Architect and develop services for real-time personalization across content feeds, product recommendations, newsletters, and marketing technology funnels. Build robust APIs and infrastructure to support dynamic recommendation use cases on web, mobile, and email. Understand and architect end-to-end implementation of key recommendation features-from data ingestion to ranking logic and delivery. Ensure high standards of performance, security, and fault tolerance in production systems. Collaborate with ML and GenAI teams to evaluate the use of large language models for dynamic content ranking, summaries, or affinity prediction. Drive integration of semantic search and LLM-based personalization where appropriate, ensuring real-time responsiveness and system scalability. Lead experimentation on AI-powered recommendation enhancements (e.g., hybrid LLM and collaborative filtering approaches).
Team Leadership & Coordination
Provide hands-on mentorship and technical direction to a blended team of full-time and contract engineers. Lead planning, scoping, and prioritization of engineering tasks in alignment with cross-functional goals. Ensure code quality, velocity, and accountability across distributed development efforts.
Build-vs-Buy Strategy & Evaluation
Drive build-vs-buy assessments for recommendation infrastructure, experimentation platforms, and personalization tooling. Evaluate and integrate third-party solutions as needed for features such as product recommendation engines and customer data platforms. Contribute to long-term architectural blueprints for personalization across Hearst's brands.
Cross-Functional Collaboration
Partner with Product, Data, and UX to define technical requirements that balance personalization sophistication with performance and privacy. Work closely with ML engineers to integrate trained models into live production flows and iterate based on real-time feedback. Collaborate with analytics and experimentation teams to validate and optimize recommendation impact.
Qualifications (What We're Looking For)
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. Demonstrated ability to lead hybrid teams and manage contract/vendor developers toward shared goals. Preferred experience delivering both content and product recommendation engines in production environments. Preferred experience deploying or integrating large language models (LLMs) into consumer-facing personalization or recommendation flows. Preferred experience working with AI model ops platforms (e.g., Vertex AI, AWS Bedrock) Benefits (What We Offer)
Work with the Best:
Collaborate with top-tier professionals across media, advertising, tech, fashion, lifestyle, and publishing, shaping the future of these dynamic industries. Grow Your Skills:
Unlock your potential with access to innovative training programs, immersive workshops, and exclusive industry events. Work-Life Harmony:
Enjoy the flexibility of hybrid work, empowering you to balance professional success with personal priorities. Foster Connection & Belonging:
Join our Employee Resource Groups and help create a welcoming workplace where everyone feels valued and empowered. Wellness First:
Prioritize your well-being with a comprehensive benefits package that includes medical, dental, and vision insurance from Day 1. Plan for Your Financial Future:
Enjoy competitive financial perks, including a 401(k) plan with a generous company match.
The base salary for this role is between $190,000 - $230,000. The actual base pay offered is dependent upon many factors, such as: transferable skills, work experience, business needs and market demands. The base pay range is subject to change and may be modified in the future.
Hearst Magazines is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, protected veteran status, or any other characteristic protected by law.