AI Engineering Manager, Notifications AI at LinkedIn3

Team Overview: The Notification AI team builds large-scale, high-quality machine learning systems that deliver the right content to the right member, through the right channel, at the right time—and at the right frequency. Our mission is to maintain a healthy, relevant, and delightful notification ecosystem that helps members achieve their professional goals while ensuring every interaction provides meaningful value. We power AI-driven systems that target, rank, and make decisions across LinkedIn’s notification portfolio, enabling product teams to deliver member value efficiently and responsibly. Notifications AI directly shapes the value members get from the platform, driving over 50% of total engagement on LinkedIn and helping millions of professionals reconnect with opportunities that matter. Our work spans large language models, recommendation systems, and scalable architectures that deepen LinkedIn’s understanding of members—their skills, journeys, and evolving intent—so we can surface the most relevant content, jobs, and connections to help them grow their professional networks meaningfully. The team has an ambitious roadmap and partners closely with Product, Engineering, and Data Science to deliver scalable, leverageable AI solutions with global member impact. If you’re looking to lead a highly visible, fast-moving, and exceptionally talented team at the intersection of cutting-edge research and real-world impact—and have fun while doing it—Notifications AI is the place for you! Location: At LinkedIn, our approach to flexible work is centered on trust and optimized for culture, connection, clarity, and the evolving needs of our business. The work location of this role is hybrid, meaning it will be performed both from home and from a LinkedIn office on select days, as determined by the business needs of the team.  This job is based in Sunnyvale, CA.  Key Responsibilities: Lead a team of AI Scientists/Engineers to build scalable notification recommendation systems at Linkedin. We’re looking for a technical manager with strong leadership, technical vision, and collaboration skills to drive impact and build great team culture. Lead and inspire the team: Manage and grow a high-performing team of researchers/applied scientists, and engineers. Attract, mentor, and develop diverse talent while fostering an inclusive, collaborative environment where people feel empowered to share ideas, take smart risks, and grow into technical leaders. Set strategy and direction: Translate product and business needs into a clear, focused technical roadmap. Ensure the team’s day-to-day work aligns with LinkedIn’s mission and long-term priorities. Partner with senior leadership to shape long-range AI and infrastructure strategy. Advance state-of-the-art recommender systems: Lead the team in developing and deploying scalable retrieval and ranking systems powered by large language models (LLMs) and advanced ML solutions to optimize notification timing and delivery decisions. Leverage LinkedIn’s rich data ecosystem to ground model outputs, improve relevance, and strengthen end-to-end notification quality. As a hands-on technical manager, guide critical technical decisions and collaborate closely with technical leaders across the organization. Ensure scalability and efficiency: Collaborate with infrastructure and platform teams retrieval and serving system performance optimizations through advanced techniques like GPU-powered retrieval-as-ranking optimization, adaptive caching, and parameter-efficient fine-tuning. Maintain high standards for reliability, scalability, and latency. Drive alignment and cross-functional collaboration: Work closely with partner teams to identify shared opportunities, align on long-term goals, and maintain consistent progress. Address misalignments proactively with clarity, empathy, and data-driven reasoning. Promote innovation and high-quality execution: Create a culture that encourages experimentation, curiosity, and continuous improvement. Ensure the team adheres to strong engineering and scientific practices, enabling rapid iteration through A/B testing and rigorous evaluation.