Sr. AI Engineering Manager, Ads AI

This role will be based in Mountain View.

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.

We’re hiring a Senior Engineering Manager to lead LinkedIn’s Ads Attribution & Companies team. You’ll own end-to-end measurement and modeling for advertiser outcomes — privacy-safe signal integration, multi-touch attribution, conversion modeling, and experimentation — while scaling the Companies data platform that powers insights across LinkedIn. You’ll set vision and roadmap, partner closely with Product, Data Science, Sales, and Legal, and deliver reliable, high-impact systems and APIs. You’ll grow and mentor an inclusive, high-performing engineering team and build a culture of clarity, ownership, and excellence.

What you’ll bring: deep experience in ads measurement/attribution, production ML (causal inference, propensity, experimentation), and large-scale distributed data systems; a track record of shipping high-quality, resilient services; and strong cross-functional leadership. Bonus: B2B data, identity graphs, offline conversions, and MMM.

If you’re excited to build the measurement backbone for LinkedIn Ads and make our companies ecosystem smarter for millions of members and customers, we’d love to talk.

Responsibilities:

• Develop a strategy for a new technical foundation based on using AI/LLMs to innovate in ad recommendation platforms.

• Design a technical solution with actionable recommendations across Marketing and Sales ecosystems, creating a unified data flywheel that compounds value by learning from advertiser outcomes and CRM closed‑won signals.

• Evangelize the use of LLMs and AI in software development in the broader organization with robust experimentation and measurement frameworks that tie model performance directly to business outcomes, not just offline or proxy metrics.

• Technical vision, execution standards, and mentorship that raise the bar for applied ML rigor, system reliability, and real‑world impact across the team.

• Partner with infra and platform teams to optimize retrieval and serving efficiency, including embedding optimization, adaptive caching, and parameter-efficient fine-tuning.

• Attract world class talent and provide technical guidance, career development, and mentoring to team members.

• Create an environment that values curiosity, diverse perspectives, and open dialogue. Encourage the team to challenge assumptions, experiment responsibly, and continuously raise the technical bar.

Department: Engineering
Experience Level: Mid-Senior Level