Machine Learning Engineer, Personalization & Recommendation Systems

Comcast • Washington, DC

2d • $142,651 - $213,977

About The Position

Make your mark at Comcast -- a Fortune 30 global media and technology company. From the connectivity and platforms we provide, to the content and experiences we create, we reach hundreds of millions of customers, viewers, and guests worldwide. Become part of our award-winning technology team that turns big ideas into cutting-edge products, platforms, and solutions that our customers love. We create space to innovate, and we recognize, reward, and invest in your ideas, while ensuring you can proudly bring your authentic self to the workplace. Join us. You’ll do the best work of your career right here at Comcast. (In most cases, Comcast prefers to have employees on-site collaborating unless the team has been designated as virtual due to the nature of their work. If a position is listed with both office locations and virtual offerings, Comcast may be willing to consider candidates who live greater than 100 miles from the office for the remote option.) Job Summary Join our Personalization team and help shape the future of customer experiences for millions of users across Comcast and Sky. We are looking for a Machine Learning Engineer with deep expertise in personalization and recommendation systems. This is an opportunity to work on cutting-edge algorithms, large-scale data systems, and deliver impactful solutions that drive engagement and satisfaction.

Requirements

• Ph.D. in Computer Science, Machine Learning, Statistics, or related field (or equivalent research experience).
• Strong background in machine learning, deep learning, and recommendation systems.
• Proficiency in Python and ML frameworks (TensorFlow, PyTorch, etc.).
• Experience with large-scale data processing (Spark, Hadoop) and cloud platforms (AWS, Azure, GCP).
• Solid understanding of algorithms, data structures, and software engineering principles.
• 5-7 Years Relevant Work Experience

Nice To Haves

• Experience with personalization systems in media, e-commerce, or streaming platforms.
• Publications in top-tier ML conferences or journals.
• Experience applying contrastive learning techniques to improve model representations and performance.
• Strong understanding of large language models (LLMs) and hands-on experience with fine-tuning for domain adaptation.
• Ability to design and implement advanced sampling strategies for machine learning tasks.
• Proven track record of working on personalization and recommendation systems at scale.
• Familiarity with evaluation methodologies across multiple domains and optimizing models for cross-domain generalization.
• Familiarity with reinforcement learning or multi-armed bandit approaches.

Responsibilities

• Design, develop, and deploy machine learning models for personalization at scale.
• Leverage contrastive learning and large language models (LLMs) to improve content recommendations.
• Contribute to research-driven innovation, such as developing advanced sampling strategies and fine-tuning LLMs for cross-domain performance improvements.
• Research and implement state-of-the-art algorithms in recommendation systems, NLP, and predictive modeling.
• Collaborate with data scientists, engineers, and product teams to integrate models into production systems.
• Optimize model performance and ensure scalability across large datasets.
• Stay current with advancements in ML and personalization technologies.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Education Level: Ph.D. or professional degree
• Number of Employees: 5,001-10,000 employees