Staff Machine Learning Engineer, Recommendation & AI Platform

Staff Machine Learning Engineer, Recommendation & AI Platform Mountain View, California, United States About News Break Founded in 2015, News Break is the Content Intelligence platform shaping the future content economy. With over 40 million monthly active users, our flagship platform delivers highly personalized local news and information powered by advanced AI, recommendation systems, and adtech. Recognized by Fast Companyas #32 on the Top Workplaces for Innovators, we're proud to be Great Place to Work® certified and home to a dynamic team of technologists, product innovators, and business leaders who are passionate about solving meaningful challenges at scale. Together, we reached unicorn status in 2021, and we remain committed to continuing this high-growth trajectory with the right team to fulfill our mission: building the infrastructure layer for content intelligence. If you’re inspired to dream big, innovate fast, and make a difference, we’d love to hear from you! For more information, visit www.newsbreak.com/about About the Role We are seeking aStaff Machine Learning Engineerto providetechnical leadershipfor our recommendation systems and AI initiatives. In this role, you will define technical direction forlarge-scale personalization systems, lead complex projects across teams, and drive the adoption ofAI / LLM technologiesin both product capabilities and engineering workflows. This role is ideal for engineers who enjoysystem-level thinking, mentoring others, and turning ambiguous problems into scalable solutions. What You’ll Work On Technical Leadership & Architecture Lead the design ofnext-generation recommendation system architecturesat scale. Drive best practices formodel lifecycle management, experimentation, and system reliability. Partner with product, infra, and data teams to align technical strategy with business goals. Advanced Recommendation & AI Systems Own complex initiatives acrossretrieval, ranking, re-ranking, and multi-objective optimization. Lead the application ofLLMs and foundation modelsin recommendation, content intelligence, and user modeling. Evaluate emerging AI technologies and guide their adoption into production systems. ChampionAI-native development practices, including LLM-assisted coding, experimentation, and analysis. Help establish standards and shared tooling for AI-driven engineering. Lead 0–1 AI initiatives, from ideation and technical validation to production rollout and scaling.