Senior Manager, Software Engineering, Machine Learning

Welcome to Warner Bros. Discovery… the stuff dreams are made of.

Who We Are…

When we say, “the stuff dreams are made of,” we’re not just referring to the world of wizards, dragons and superheroes, or even to the wonders of Planet Earth. Behind WBD’s vast portfolio of iconic content and beloved brands, are the storytellers bringing our characters to life, the creators bringing them to your living rooms and the dreamers creating what’s next…

From brilliant creatives, to technology trailblazers, across the globe, WBD offers career defining opportunities, thoughtfully curated benefits, and the tools to explore and grow into your best selves. Here you are supported, here you are celebrated, here you can thrive.

We are the now and the next. The power behind the people building the future. We are born from the spirit of innovation. We are created from the idea that people around the world want more, need more, deserve more. We are the home of the global digital revolution. We are CNN.

To see what it’s like to work at CNN, follow @WBDLife on Instagram and X!

With deep domain expertise, advanced technical capabilities, and a proven track record of successful collaborations, the AI Enablement & Machine Learning team at CNN is accelerating our digital transformation through strategic applications of machine learning and AI technologies.

The Machine Learning group within this team develops, deploys, and optimizes ML models that drive measurable business impact through better engagement and growth. The team owns two workstreams — Engagement ML, which powers personalized recommendations, content reranking, and search for CNN's news audiences, and Growth ML, which drives revenue and user value through product recommendations, advertising optimization, and commercial experiences. The team uses rigorous experimentation to prove value and inform product strategy across all initiatives.

Our vision is that CNN's ML models drive measurable business impact through better engagement and growth, with rigorous experimentation that proves value and informs product strategy.

Your New Role...

The Machine Learning group is composed of two workstreams — Engagement ML and Growth ML. Engagement ML builds models that increase user engagement through personalized recommendations, intelligent page layouts, and content discovery. Growth ML builds models that drive business growth through product recommendations, advertising optimization, and conversion-focused experiences.

As the Senior Engineering Manager for Machine Learning, you will oversee both workstreams, managing an Engineering Manager for Engagement ML and hiring an Engineering Manager for Growth ML, with a combined team growing to approximately 12 engineers through 2026. You will partner with the organization's Senior Technical Program Manager for cross-team coordination and delivery.

Key challenges the team will tackle:

Engagement ML:

• Homepage Personalization: Launch personalization features on the web and app homepage that measurably improve engagement metrics
• Content Reranking: Deliver content and container reranking to production with demonstrated impact on user engagement
• All Access: Power search and recommendations for the All Access subscriber experience
• Editorial Signal Integration: Incorporate editorial signals (trending, breaking news, quality indicators) into ML models while maintaining editorial integrity

Growth ML:

• AI-Powered Commerce: Launch new AI-powered experiences in partnership with the Underscored team
• Unified Page Layout Optimization: Establish a partnership with the ads team to balance ad revenue and content engagement through a single ML-driven system
• Ad Modeling: Support DART on ad modeling initiatives to improve targeting, yield, and revenue optimization

Your Role Accountabilities...

• Support, coach, mentor, and provide valuable feedback to the individuals you manage to help them excel — across a range of roles including machine learning engineers, software engineers, and engineering managers as the team grows
• Manage and develop the Engagement ML Engineering Manager and hire and onboard a Growth ML Engineering Manager, building a strong leadership bench
• Work with cross-functional partners and stakeholders on activities such as planning, technical strategy, quality, and delivery
• Partner with product, editorial, and business teams on feature requirements, success metrics, and experimentation design — translating business goals into ML problem formulations
• Partner with AI Systems & Enablement for production integration and application logic
• Partner with ML Platform and Data Platform teams for deployment infrastructure, experimentation framework, feature engineering, and training data
• Drive experimentation methodology and statistical rigor across all ML initiatives, ensuring the team proves impact through data
• Determine the right modeling approach for each use case — custom model vs. foundation model, algorithm selection, and feature engineering strategy
• Lead other engineers on the team by example, code reviews, and coaching, ensuring that code is readable, maintainable, scalable, observable, and resilient
• Champion improvements to developer experience, integrations, and testing processes
• Leverage vended and open-source cloud technologies to reduce maintenance costs and improve efficiencies, ensuring our products remain cost-effective
• Build and scale the team through hiring, onboarding, and establishing team norms for a rapidly growing group

Here Is the Approach We Value...

• Review and provide guidance on production-quality code that adheres to industry standards and best practices (IaC, CI/CD, etc.)
• Demonstrate a passion for ML-powered product development, with a strong sense of responsibility for the quality and measurable impact of what your team ships
• Take ownership of issues and be a strong advocate for your team and the products
• Embrace failure as a learning opportunity — use research and experimentation to ultimately choose the best solutions that meet company goals
• Follow a progressive development methodology, moving from proof-of-concept to prototype to production release
• Enhance the effectiveness of your team and our partners by sharing your knowledge, communicating about complex ML technologies and problems in simple terms, and driving technical decisions
• Collaborate across functions, squads, teams, and organizations to best serve our users

Qualifications & Experience...

• 8+ years of progressively complex experience designing, building, and shipping products
• 5+ years of experience managing engineering teams, including experience managing managers
• 5+ years of experience in machine learning — model development, training, optimization, and deployment in production systems
• 3+ years of experience with cloud infrastructure, deployment pipelines, and production ML serving
• Strong understanding of ML fundamentals — recommendation systems, ranking, personalization, and experimentation/A/B testing methodology
• Experience determining modeling approaches across a range of techniques (two-tower models, bandits, LLM-based systems, traditional ML)
• Experience with experimentation frameworks, statistical rigor, and data-driven decision making
• A proven track record of building ML-powered features that drive measurable business metrics
• Passion for growing engineers through mentorship, talent acquisition, and professional development
• Experience scaling teams — hiring, onboarding, and establishing culture and processes for a growing group
• A collaborative mindset, understanding that great results come from teamwork and a positive culture
• Proven success leading complex projects within an engineering team: knowing when to handle issues independently, when to rally the right people for alignment, and when to escalate
• Comfort navigating ambiguity in a fast-evolving space — the intersection of ML and product strategy requires balancing rigor with speed
• Excellent communication, presentation, and documentation skills

The Nice to Haves...

• Experience building recommendation systems, personalization, or search relevance at scale
• Experience with page layout optimization or multi-objective ranking (e.g., balancing engagement and revenue)
• Experience partnering with editorial teams in a news or media environment
• Familiarity with content understanding models (classification, entity extraction, categorization)
• Experience with advertising optimization, yield management, or commercial ML applications
• Experience managing managers and building a leadership bench within an ML organization

How We Get Things Done…

This last bit is probably the most important! Here at WBD, our guiding principles are the core values by which we operate and are central to how we get things done. You can find them at www.wbd.com/guiding-principles/ along with some insights from the team on what they mean and how they show up in their day to day. We hope they resonate with you and look forward to discussing them during your interview.

Championing Inclusion at WBD

Warner Bros. Discovery embraces the opportunity to build a workforce that reflects a wide array of perspectives, backgrounds and experiences. Being an equal opportunity employer means that we take seriously our responsibility to consider qualified candidates on the basis of merit, without regard to race, color, religion, national origin, gender, sexual orientation, gender identity or expression, age, mental or physical disability, and genetic information, marital status, citizenship status, military status, protected veteran status or any other category protected by law.

If you’re a qualified candidate with a disability and you require adjustments or accommodations during the job application and/or recruitment process, please visit our accessibility page for instructions to submit your request.