Principal Product Manager, Feed Relevance

This role will be based in Mountain View / San Francisco, CA or Bellevue, WA.

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 with voices, 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-driven ranking and recommendation systems that power 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 apply machine learning and foundation models to improve relevance, member value, and ecosystem health. You’ll partner deeply with AI Research, Engineering, Data Science, and Infrastructure teams to build end-to-end ranking systems, from problem framing and feature strategy to model evaluation and member experience optimization.

You’ll operate at the intersection of AI innovation and product impact, helping to set the vision for how members engage with content, conversations, and creators on LinkedIn.

Responsibilities

• Partner with PMs, Applied Research, Data Science, and Infrastructure Engineering to define and execute a comprehensive AI roadmap for Feed Relevance, balancing innovation with measurable member impact.
• Translate member experience goals and business outcomes into clear model objectives and success metrics, ensuring alignment between algorithmic decisions and product strategy.
• Drive the prioritization, definition, and delivery of new features, model improvements, and system capabilities that advance LinkedIn’s Feed ranking quality and personalization.
• Lead cross-functional product development, from requirements and design (PRDs, policy alignment, evaluation frameworks) through to experimentation and launch.
• Partner with AI platform and applications teams to scale model training, inference, and evaluation infrastructure that supports rapid iteration and continuous learning.
• Use data and experimentation to guide decision-making, defining hypotheses, running A/B tests, and synthesizing insights to inform model and product improvements.
• Stay ahead of the competitive landscape in AI-driven relevance and personalization, identifying opportunities to differentiate LinkedIn’s Feed experience.
• Communicate strategy, priorities, and outcomes clearly to executive stakeholders and cross-functional partners, shaping how LinkedIn’s Feed evolves to deliver trusted, high-value engagement.

Basic Qualifications

• BS/BA Degree in a technology related field OR equivalent experience
• 8+ years of experience in product management or equivalent role

Preferred Qualifications

• 10+ years of experience in product management
• Understanding of machine learning, ranking algorithms, and recommender systems, with experience applying them to large-scale Feed or content relevance products.
• Experience in delivering complex ML-driven systems end-to-end, from problem definition and data strategy to model deployment, evaluation, and iteration.
• Ability to translate model and system performance into clear member value, business impact, and ecosystem health outcomes.
• Demonstrated success defining and executing AI product strategy in partnership with Applied Research, Engineering, and Data Science teams.
• Experience collaborating with AI platform and infrastructure teams to scale experimentation, feature development, and model training capabilities.
• Strong expertise in A/B testing, causal inference, and multi-objective optimization within ranking or recommendation contexts.
• Ability to communicate and simplify complex ML concepts and influence at the executive and cross-functional level.
• Prior experience building or evolving Feed, Search, or Discovery systems that drive trust, relevance, and meaningful member engagement.

 Suggested Skills

• Applied AI/ML
• Feed Ranking & Relevance
• Recommender Systems
• Generative AI

 

LinkedIn is committed to fair and equitable compensation practices.   

The pay range for this role is $185,000 to $299,000.  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 https://careers.linkedin.com/benefits.

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 at [email protected] and describe the specific accommodation requested for a disability-related limitation.

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: https://lnkd.in/paytransparency.

Global Data Privacy Notice for Job Candidates ​

Please follow this link to access the document that provides transparency around the way in which LinkedIn handles personal data of employees and job applicants: https://legal.linkedin.com/candidate-portal.