Job description

This role will be based in Mountain View or San Francisco. 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. At LinkedIn, our mission is to connect the world’s professionals to make them more productive and successful. The Feed team sits at the heart of that mission, helping members discover ideas, connect, and find opportunity. As a Principal Product Manager on the Feed Relevance team, you’ll shape how cutting-edge machine learning and foundation models connect hundreds of millions of professionals to what matters most, helping define the future of relevance, trust, and engagement on LinkedIn. We’re looking for a Principal Product Manager to lead the next generation of AI-powered content understanding and quality systems that shape the LinkedIn Feed. This is a senior individual contributor role for a proven product leader who can define bold strategy, influence across disciplines, and deliver at massive scale. In this role, you’ll shape how we leverage machine learning, large language models, and foundation models to understand the content shared on LinkedIn and translate that understanding into more relevant, valuable, and trustworthy recommendations. You’ll drive systems that identify high-quality content, surface meaningful professional insights, and reduce the distribution of low-quality or harmful content, balancing member value, engagement, and long-term ecosystem health. You’ll partner deeply with AI Research, Engineering, Data Science, and Trust & Safety teams to develop and scale content understanding capabilities that power Feed ranking and recommendations. Beyond content understanding, you’ll have opportunities to influence the broader evolution of LinkedIn’s recommendation systems and deliver meaningful improvements to how millions of members discover content, conversations, and professional perspectives. Responsibilities : • Define and execute the AI product strategy for content understanding and quality across LinkedIn Feed, partnering with PMs, Applied Research, Engineering, Data Science, and Trust & Safety to deliver measurable improvements in relevance, member value, and ecosystem health. • Lead the development of large-scale ML and foundation model-powered systems that understand content topics, intent, quality, credibility, and professional value, translating these insights into actionable signals for ranking and recommendation systems. • Drive the evolution of content quality and recommendation policies that amplify valuable, authentic content while reducing low-quality, misleading, or harmful content, balancing short-term engagement with long-term member trust and ecosystem health. • Translate member experience goals and business outcomes into clear model objectives, evaluation frameworks, and success metrics, ensuring alignment between AI capabilities, recommendation quality, and product strategy. • Partner with Applied Research and Infrastructure Engineering to advance scalable content understanding capabilities, including LLM-based classification, content representations, model evaluation, and efficient inference at LinkedIn scale. • 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 continuously improve content quality and Feed relevance. • Collaborate with Feed ranking and personalization teams to integrate content understanding signals into end-to-end recommendation systems, influencing how content is retrieved, ranked, and distributed to members. • Stay ahead of advances in AI, content understanding, and recommendation systems, identifying opportunities to leverage emerging technologies to differentiate LinkedIn’s Feed experience. • Communicate product strategy, priorities, tradeoffs, and outcomes clearly to executive stakeholders and cross-functional partners, influencing the broader direction of LinkedIn’s AI-powered recommendations. Basic Qualifications: • BS/BA degree in a technology-related field or equivalent practical experience. • 8+ years of experience in product management or an equivalent role. Preferred Qualifications: • MS degree in Computer Science, Machine Learning, or a related technical field. • 10+ years of experience in product management, including experience 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. • Proven track record of delivering complex ML-driven systems end-to-end, from problem definition and data strategy to model 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. • Strong understanding of the tradeoffs between relevance, engagement, content quality, creator incentives, and long-term ecosystem health in recommendation systems. • Demonstrated ability to define and execute AI product strategy in close partnership with Applied Research, Engineering, Data Science, and Infrastructure teams. • Experience working with large-scale AI systems, including model evaluation, human labeling, training data quality, and inference efficiency. • Strong expertise in experimentation, A/B testing, causal inference, and multi-objective optimization within ranking or recommendation systems. • Exceptional communication and cross-functional leadership skills, with the ability to simplify complex technical concepts, influence senior stakeholders, and drive alignment across teams. • Passion for 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 LinkedIn is committed to fair and equitable compensation practices. The pay range for this role is $187,000 to $305,000. Actual compensation packages are based on several factors that are unique to each candidate, including but not limited to skill set, depth of experience, certifications, and specific work location. This may be different in other locations due to differences in the cost of labor. The total compensation package for this position may also include annual performance bonus, stock, benefits and/or other applicable incentive compensation plans. For more information, visit careers.linkedin.com. Equal Opportunity Statement We seek candidates with a wide range of perspectives and backgrounds and we are proud to be an equal opportunity employer. LinkedIn considers qualified applicants without regard to race, color, religion, creed, gender, national origin, age, disability, veteran status, marital status, pregnancy, sex, gender expression or identity, sexual orientation, citizenship, or any other legally protected class. LinkedIn is committed to offering an inclusive and accessible experience for all job seekers, including individuals with disabilities. Our goal is to foster an inclusive and accessible workplace where everyone has the opportunity to be successful. If you need a Reasonable Accommodation to search for a job opening, apply for a position, or participate in the interview process, connect with us and describe the specific Accommodation requested for a disability-related limitation. Fill out an Accommodation request here: app.smartsheet.com Reasonable accommodations are modifications or adjustments to the application or hiring process that would enable you to fully participate in that process. Examples of reasonable accommodations include but are not limited to: • Documents in alternate formats or read aloud to you • Having interviews in an accessible location • Being accompanied by a service dog • Having a sign language interpreter present for the interview A request for an accommodation will be responded to within three business days. However, non-disability related requests, such as following up on an application, will not receive a response. LinkedIn will not discharge or in any other manner discriminate against employees or applicants because they have inquired about, discussed, or disclosed their own pay or the pay of another employee or applicant. However, employees who have access to the compensation information of other employees or applicants as a part of their essential job functions cannot disclose the pay of other employees or applicants to individuals who do not otherwise have access to compensation information, unless the disclosure is (a) in response to a formal complaint or charge, (b) in furtherance of an investigation, proceeding, hearing, or action, including an investigation conducted by LinkedIn, or (c) consistent with LinkedIn's legal duty to furnish information. San Francisco Fair Chance Ordinance Pursuant to the San Francisco Fair Chance Ordinance, LinkedIn will consider for employment qualified applicants with arrest and conviction records. Pay Transparency Policy Statement As a federal contractor, LinkedIn follows the Pay Transparency and non-discrimination provisions described at this link: lnkd.in. Global Data Privacy Notice and Compliance Posters for Job Candidates Please use this link to access documents that provide information about how LinkedIn handles the personal data of employees and job applicants, as well as the E-Verify Participation Notice and the Department of Justice Immigrant and Employee Rights Section Right to Work posters: linkedin.com.

Requirements

Must-haves

Translate member experience goals and business outcomes into clear model objectives, evaluation frameworks, and success metrics, ensuring alignment between AI capabilities, recommendation quality, and product strategy.
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 continuously improve content quality and Feed relevance.
Collaborate with Feed ranking and personalization teams to integrate content understanding signals into end-to-end recommendation systems, influencing how content is retrieved, ranked, and distributed to members.
Stay ahead of advances in AI, content understanding, and recommendation systems, identifying opportunities to leverage emerging technologies to differentiate LinkedIn’s Feed experience.
Communicate product strategy, priorities, tradeoffs, and outcomes clearly to executive stakeholders and cross-functional partners, influencing the broader direction of LinkedIn’s AI-powered recommendations.
BS/BA degree in a technology-related field or equivalent practical experience.
8+ years of experience in product management or an equivalent role.
10+ years of experience in product management, including experience leading complex AI/ML-driven products at scale.
Applied AI/ML
Content Understanding & Classification
Content Quality & Ecosystem Health
Feed Ranking & Relevance

Nice-to-haves

MS degree in Computer Science, Machine Learning, or a related technical field.
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.
Proven track record of delivering complex ML-driven systems end-to-end, from problem definition and data strategy to model 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.
Strong understanding of the tradeoffs between relevance, engagement, content quality, creator incentives, and long-term ecosystem health in recommendation systems.
Demonstrated ability to define and execute AI product strategy in close partnership with Applied Research, Engineering, Data Science, and Infrastructure teams.
Experience working with large-scale AI systems, including model evaluation, human labeling, training data quality, and inference efficiency.
Strong expertise in experimentation, A/B testing, causal inference, and multi-objective optimization within ranking or recommendation systems.
Exceptional communication and cross-functional leadership skills, with the ability to simplify complex technical concepts, influence senior stakeholders, and drive alignment across teams.
Passion for building AI-powered experiences that help members discover trustworthy, relevant, and professionally valuable content.

Application prep

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Keywords to mirror

machine learningaimlllm

Platform: smartrecruiters
Effort: medium

Benefits

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