Director, Machine Learning Engineering, CNN Digital Products & Services

Warner Bros. Discovery•Washington, DC

23h

About The Position

The ML & AI team at CNN is accelerating digital transformation through strategic applications of ML and AI technologies, applying optimization to everything from content recommendations to ad sales to subscription growth—serving millions of CNN users via web and mobile apps. The Director, ML / AI leads this technical and people agenda: setting direction for ML and AI across CNN’s digital products, aligning roadmaps with engineering, analytics, product, and editorial partners, and ensuring that what is shipped is measurable, reliable, and worthy of a global news brand.

Requirements

Advanced degree (Master’s or PhD) in Computer Science, Mathematics, Statistics, Information Science, Engineering, or another quantitative discipline—or equivalent depth from industry experience.
8+ years of professional experience building machine learning and data-science-driven systems with a proven record of innovation and measurable product or business impact. (Candidates with a PhD may qualify with 6+ years of strong industry experience post-degree if the depth is equivalent.)
5+ years developing and deploying AI/ML systems across the full lifecycle—ideation, proof of concept, deployment, monitoring, and iteration—in Python.
Demonstrated technical leadership: a sustained record of leading multi-engineer initiatives, setting technical direction for ML systems, mentoring senior engineers, and partnering across product and platform—through people management, staff/principal-level scope, or an equivalent combination.
Experience deploying complex, large-scale machine learning systems in production; practical familiarity with ML modeling and the operational realities of live models.
Strong communication skills and the ability to build trusted partnerships across functions in a fast-paced, high-energy environment.

Nice To Haves

Experience from large-scale consumer internet environments—social, streaming, marketplaces, or similar—where ranking, retrieval, recommendations, or search operate under latency, safety, and experimentation constraints.
Experience with recommendation, search, or ad-serving algorithms and systems; NLP, information retrieval, or related areas.
Familiarity with data pipelines, feature stores, embedding infrastructure, or large-scale feature serving.
Deep familiarity with experimentation frameworks and A/B testing methodologies.
Interest in media, publishing, or news; commitment to responsible AI and high-trust content experiences.
Exposure to generative AI in product settings—guardrails, evaluation, latency and cost tradeoffs, human-in-the-loop workflows.
Experience with Agile delivery practices.

Responsibilities

Ensure excellent ML/AI methods for CNN’s consumer and editorial surfaces: a coherent portfolio across personalization, search, recommendations, content understanding, and responsible use of generative AI and foundational models.
Combine people leadership with credible technical judgment: build and develop machine learning engineers, set cross-team priorities, and stay hands-on enough to arbitrate architecture and modeling decisions when it matters.
Partner with product and editorial teams to develop new uses for machine learning and AI tools across CNN’s internal and customer-facing environments.
Co-own roadmaps with data engineering and analytics so instrumentation, pipelines, feature paths, telemetry, and data quality keep pace with ML.
Champion rigorous testing, and machine-learned targeting, in coordination with Data & Analytics, and clear decision rights so we learn fast without shipping confusion.
Ensure ML-powered experiences work within real-time and web-scale constraints—alongside caching, edge delivery, and high availability for millions of users.
Balance innovation with maintainability and cost—leverage cloud and open-source capabilities responsibly.
Lead, hire, and develop machine learning engineers; foster an inclusive culture that provides career growth and guidance for MLEs at all levels while pushing the boundaries on new methodologies and technologies.
Partner with product and analytical teams to pioneer new uses of ML and optimize existing use cases.
Own the ML systems roadmap with platform, data, and product leadership; size opportunities, prioritize capabilities, and secure alignment and resourcing with senior stakeholders.
Partner with platform, infrastructure, and ML platform teams on training, serving, feature pipelines, and operational needs for personalization and search.
Partner with downstream application teams on stable interfaces between ML capabilities and the website, mobile apps, and editorial tools.
Guide design and delivery of ML in production: monitoring, validation, iteration based on live impact—not offline metrics alone.
Represent ML/AI clearly to editorial and business audiences; translate goals into investments and non-negotiables (trust, corrections, safety).
Respond to incidents and learning cycles at the organizational level; champion improvements to developer experience, integrations, and testing.
Author, review, and optimize production-quality code and systems that adhere to industry standards and best practices.
Demonstrate passion for software and ML engineering, with a strong sense of responsibility for what you and your team ship.
Take ownership of issues and advocate for your team and products; embrace failure as a learning opportunity and use research and experimentation to choose solutions that meet company goals.
Follow a progressive development methodology—proof-of-concept to prototype to production—with measurable milestones.
Enhance the effectiveness of your team and partners by sharing knowledge, explaining complex technologies in simple terms, and driving sound technical decisions.
Collaborate across functions, squads, teams, and organizations to best serve our users.