Principal Product Manager - AI Content Understanding & Feed Relevance
About The Position
LinkedIn is seeking a Principal Product Manager for the Feed Relevance team to lead AI-powered content understanding and quality systems that shape the LinkedIn Feed. This senior individual-contributor role will define product strategy and influence how machine learning, large language models, and foundation models help connect professionals with relevant, valuable, and trustworthy content. The role is based in Mountain View or San Francisco and is hybrid, with work performed from home and a LinkedIn office on select days according to business needs. The product manager will develop systems that understand content and use that understanding to improve recommendations: identifying high-quality content and meaningful professional insights while reducing low-quality or harmful distribution. The role partners closely with AI Research, Applied Research, Engineering, Data Science, Infrastructure Engineering, Trust & Safety, and Feed ranking and personalization teams. It also helps shape broader recommendation systems and improve how members discover content, conversations, and professional perspectives.
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 deployment, evaluation, experimentation, and iteration.
- Experience translating content quality, trust, and safety objectives into scalable product policies, model capabilities, and 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 the ability to explain complex technical concepts, influence senior stakeholders, and drive alignment across teams.
- Interest in building AI-powered experiences that help members discover trustworthy, relevant, and professionally valuable content.
- Suggested skills: Applied AI/ML; Content Understanding & Classification; Content Quality & Ecosystem Health; Feed Ranking & Relevance; Recommender Systems.
Responsibilities
- Define and execute the AI product strategy for content understanding and quality across LinkedIn Feed, collaborating with product, research, engineering, data science, and Trust & Safety teams to improve relevance, member value, and ecosystem health.
- Lead development of large-scale machine-learning and foundation-model systems that understand content topics, intent, quality, credibility, and professional value, and convert those insights into signals for ranking and recommendations.
- Evolve content quality and recommendation policies to amplify valuable, authentic content and reduce low-quality, misleading, or harmful content while balancing engagement, member trust, and long-term ecosystem health.
- 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, evaluate model and policy performance, and improve content quality and Feed relevance.
- Collaborate with Feed ranking and personalization teams to integrate content-understanding signals into recommendation systems and influence content retrieval, ranking, and distribution.
- Monitor advances in AI, content understanding, and recommendation systems and identify opportunities to apply emerging technologies to LinkedIn Feed.
- Communicate strategy, priorities, tradeoffs, and outcomes to executive stakeholders and cross-functional partners, influencing the broader direction of AI-powered recommendations.
Benefits
- Annual performance bonus, stock, benefits, and/or other applicable incentive compensation plans may be included in the total compensation package.
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What This Job Offers
Job Type
Full-time
Career Level
Principal
