Director, Machine Learning Engineering, CNN Digital Products & Services

• 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 

Here are some of the things you can expect to do on a day-to-day basis: 

• 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. 

Here is the approach we value: 

• 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. 

Qualifications & Experience... 

• 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. 

The 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.