Principal Machine Learning Engineer

Comcast•Philadelphia, PA

4d•Hybrid

About The Position

Comcast’s AI Search and Recommendation team is building the next generation of intelligent, personalized experiences across Comcast, Sky, and NBCUniversal. We’re looking for a Principal Machine Learning Engineer to lead the design and evolution of large-scale AI platforms powering search, ranking, and recommendations used by millions of customers. This is a high-impact, hands-on leadership role where you’ll shape technical strategy, build production-grade ML systems, and drive innovation in areas like personalization, generative AI, and real-time decisioning. You’ll work at the intersection of applied research and engineering, turning cutting-edge ideas into scalable products that directly influence customer experience and business outcomes.

Requirements

10+ years of experience in machine learning, AI, or software engineering
Proven track record building and scaling production ML systems
Strong Python programming skills with a focus on performance and reliability
Experience translating advanced models or research into real-world products
Background in search, ranking, recommendations, or personalization systems
Familiarity with LLMs, generative AI, or agent-based systems
Bachelor's Degree: Computer and Information Science (or equivalent combination of coursework and experience, or extensive related professional experience)
Business Acumen
Communication
Design
Learning Agility
Machine Learning (ML)
Technical Leadership

Responsibilities

Lead AI Platform Strategy and Innovation: Own the architecture, roadmap, and evolution of core ML platforms supporting search, ranking, and recommendation systems. Define technical direction and deliver scalable solutions that enable personalization and relevance at massive scale.
Drive High-Impact Programs: Lead complex, revenue-driving initiatives where machine learning is a key differentiator. Partner closely with Product, Engineering, and Data teams to align ML capabilities with business goals.
Build Next-Generation AI Capabilities: Advance modern AI approaches including: Recommender systems and learning-to-rank models, Generative AI, LLM fine-tuning, and prompt engineering, Agentic AI systems integrating models with tools and workflows. Stay on the leading edge of AI and rapidly translate innovation into production systems.
Architect and Scale ML Systems: Design and deploy distributed ML systems and real-time inference pipelines. Build end-to-end machine learning solutions—from data pipelines to model deployment, monitoring, and optimization.
Stay Hands-On: Write and review production-quality code across core platforms and experimental projects. Lead technical design discussions and drive engineering excellence across teams.
Influence Across the Organization: Mentor engineers and act as a technical leader across multiple teams. Establish best practices, reusable frameworks, and standards to scale innovation.