Director, Machine Learning Engineering, CNN Digital Products & Services

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Who We Are...

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Who We Are...

We are CNN. THE WORLD'S MOST ESSENTIAL AND ENGAGING SOURCE OF DIGITAL NEWS. We are in the midst of rapid transformation and need our next generation of innovators, makers, and dreamers who will lead and drive our growth. We aim to make the world a better, more connected place.

About the Team

With deep domain expertise, advanced technical capabilities, and a proven track record of successful collaborations, the ML & AI team at CNN is accelerating our 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.

Your New Role...

The Director, ML / AI leads this technical and people agenda: you set direction for ML and AI across CNN's digital products, align roadmaps with engineering, analytics, product, and editorial partners, and ensure that what we ship is measurable, reliable, and worthy of a global news brand.

Your Role Accountabilities...

You will be responsible for ensuring 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. You combine people leadership with credible technical judgment: you build and develop machine learning engineers, set cross-team priorities, and stay hands-on enough to arbitrate architecture and modeling decisions when it matters.

• ML and AI thought leadership: Partner with product and editorial teams to develop new uses for machine learning and AI tools across CNN's internal and customer-facing environments.

• Platform and data partnership: Co-own roadmaps with data engineering and analytics so instrumentation, pipelines, feature paths, telemetry, and data quality keep pace with ML-without excellent data and platform partnership, ML leadership cannot succeed.

• Experimentation at scale: Champion rigorous testing, and machine-learned targeting, in coordination with Data & Analytics, and clear decision rights so we learn fast without shipping confusion.

• Performance and scale: Ensure ML-powered experiences work within real-time and web-scale constraints-alongside caching, edge delivery, and high availability for millions of users.

• Sustainability: 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.

• Advanced degree (Master's or PhD) in Computer Science, Mathematics, Statistics, Information Science, Engineering, or another quantitative discipline-or equivalent depth from industry experience. PhDs and research-heavy backgrounds are welcome.

• 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. Formal people-management experience is valued but not strictly required for candidates with an exceptional senior or staff ML track record who are ready to step into a director role.

• 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.

• 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.