Principal Product Manager, Feed Relevance

• Full-time
• Workplace Type:
  Hybrid
• Department:
  Product

Linked In is the world’s largest professional network, built to create economic opportunity for every member of the global workforce. Our products help people make powerful connections, discover exciting opportunities, build necessary skills, and gain valuable insights every day. We’re also committed to providing transformational opportunities for our own employees by investing in their growth. We aspire to create a culture that’s built on trust, care, inclusion, and fun – where everyone can succeed.

Join us to transform the way the world works.

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

At Linked In, 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 Linked In office on select days, as determined by the business needs of the team.

At Linked In, 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 Linked In.

We’re looking for a Principal Product Manager to lead the next generation of AI-driven ranking and recommendation systems that power the Linked In 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 Linked In.

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 Linked In’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 Linked In’s Feed experience.
• Communicate strategy, priorities, and outcomes clearly to executive stakeholders and cross-functional partners, shaping how Linked In’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…