Principal Product Manager - AI Content Understanding & Feed Relevance

LinkedIn • Sunnyvale, CA

1d • Hybrid

About The Position

LinkedIn is hiring a Principal Product Manager for the Feed Relevance team, based in Mountain View or San Francisco. The position is hybrid, with work performed both from home and at a LinkedIn office on select days according to business needs. This senior individual-contributor role will shape AI-powered content understanding and quality systems for the LinkedIn Feed, helping connect members with relevant, valuable, and trustworthy professional content.

The Product Manager will define and execute strategy for applying machine learning, large language models, and foundation models to understand content and improve Feed ranking and recommendations. The role will lead development of systems that identify content topics, intent, quality, credibility, and professional value; amplify valuable and authentic content; and limit low-quality, misleading, or harmful content. Success requires balancing member value and engagement with long-term trust and ecosystem health.

The role partners closely with AI Research, Applied Research, Engineering, Infrastructure, Data Science, Trust & Safety, Feed ranking, and personalization teams. Responsibilities span problem definition, requirements, policy alignment, experimentation, model deployment, launch, evaluation, and iteration. The Product Manager will also communicate strategy and outcomes to executive stakeholders and help shape the broader evolution of LinkedIn’s recommendation systems.

Requirements

• BS/BA degree in a technology-related field or equivalent practical experience.
• At least 8 years of experience in product management or an equivalent role.

Nice To Haves

• MS degree in Computer Science, Machine Learning, or a related technical field.
• At least 10 years of product management experience, including leading complex AI/ML-driven products at scale.
• Deep understanding of machine learning, ranking algorithms, and recommender systems, with experience building or improving large-scale Feed, Search, Discovery, or content recommendation products.
• Experience developing or leveraging content understanding systems, including content classification, semantic understanding, embeddings, LLMs, multimodal models, or content quality assessment.
• Track record of delivering complex ML-driven systems end-to-end, from problem definition and data strategy through model deployment, evaluation, experimentation, and iteration.
• Experience translating content quality, trust, and safety objectives into scalable product policies and model capabilities, with measurable improvements in member experience and ecosystem health.
• Understanding of tradeoffs among relevance, engagement, content quality, creator incentives, and long-term ecosystem health in recommendation systems.
• Ability to define and execute AI product strategy in partnership with Applied Research, Engineering, Data Science, and Infrastructure teams.
• Experience with large-scale AI systems, including model evaluation, human labeling, training data quality, and inference efficiency.
• Expertise in experimentation, A/B testing, causal inference, and multi-objective optimization in ranking or recommendation systems.
• Exceptional communication and cross-functional leadership skills, including simplifying complex technical concepts, influencing senior stakeholders, and driving alignment.
• Passion for building AI-powered experiences that help members discover trustworthy, relevant, and professionally valuable content.

Responsibilities

• Define and execute the AI product strategy for content understanding and quality across LinkedIn Feed, partnering with product, research, engineering, data science, and Trust & Safety teams.
• Lead development of large-scale machine-learning and foundation-model systems to understand content topics, intent, quality, credibility, and professional value, and turn these insights into signals for ranking and recommendations.
• Evolve content quality and recommendation policies to amplify valuable, authentic content while reducing low-quality, misleading, or harmful content.
• Translate member experience goals and business outcomes into model objectives, evaluation frameworks, and success metrics.
• Partner with Applied Research and Infrastructure Engineering to advance scalable content understanding capabilities, including LLM-based classification, content representations, model evaluation, and efficient inference.
• Lead cross-functional product development from problem definition and requirements through experimentation, policy alignment, model deployment, and launch.
• Use data, experimentation, and qualitative insights to identify opportunities and continuously improve content quality and Feed relevance.
• Collaborate with Feed ranking and personalization teams to integrate content understanding signals into recommendation systems.
• Track advances in AI, content understanding, and recommendation systems and identify opportunities to use emerging technologies in the Feed experience.
• Communicate product strategy, priorities, tradeoffs, and outcomes to executive stakeholders and cross-functional partners.

Benefits

• Annual performance bonus
• Stock
• Benefits and/or other applicable incentive compensation plans

What This Job Offers

• Job Type: Full-time
• Career Level: Principal