Senior AI Engineering Manager, Ads AI

Company Description

LinkedIn 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.

Job Description

Location:

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.

This role will be based in Sunnyvale, CA. 

Team Overview: 

This role is a part of our LinkedIn Ads AI team, and will be responsible for leading roughly 20 engineers (with one manager reporting to this person, in addition to multiple technical leads). The team includes both AI/ML and Full Stack engineers, who build algorithms, models, and systems that power our Ad Tech platform and products.

Some of our next generation AI solutions include building a new B2B recommendation engine which leverages LinkedIn’s economic graph, to drive new and better results related to identity, engagement, and buyer data for our Ads products.

LinkedIn Ads is a multi-billion dollar revenue stream for the organization and is a highly successful product area.
 

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.

Qualifications

Basic Qualifications:

• Masters in Computer Science or related technical field or equivalent technical experience

• 7+ years of industry experience 

• 3+ years people management experience
   

Preferred Qualifications: 

• 10+ years of relevant work experience including 4+ years of leadership experience 

• PhD in Computer Science, Machine Learning, Statistics or related fields

• Experience with retrieval and context-heavy reasoning (RAG), fine-tuning small language models (SLMs), and customizing deep learning models for Ads ranking, retrieval, budget forecasting and control, and low latency tasks

Suggested Skills:

• AI Modeling - LLMs

• People Leadership 

• Change Management 

• Technical Strategy 

Additional Information

You will Benefit from our Culture:

We strongly believe in the well-being of our employees and their families. That is why we offer generous health and wellness programs and time away for employees of all levels.

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Compensation:

LinkedIn is committed to fair and equitable compensation practices. The pay range for this role is $198,000 - $326,000. Actual compensation packages are based on a wide array of factors unique to each candidate, including but not limited to skill set, years & depth of experience, certifications and specific office location. This may differ in other locations due to cost of labor considerations. The total compensation package for this position may also include annual performance bonus, stock, benefits and/or other