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Senior Staff AI Engineer | Senior Technical Lead - AI Modeling

Posted February 27, 2026
Full-time Mid-Senior Level

Job Overview

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.

Overview:

This role is a Senior Technical Leader (Uber TL, L7) with scope over a team of 15-25 engineers. Solutions will vary and can encompass GAI, Reinforcement Learning, Deep Learning, modern recommendations / relevance systems and algorithms, and much more - often with LLMs and multi-modal framework as an underpinning. Work can span many domains such as Search, Ads, Notifications, Feed, and Agentic solutions. 

Advanced modeling skills are required, and the work will include expert hands-on contribution in addition to thought and technical leadership. 

Responsibilities:

  • This is a Senior Technical Leader role that will provide thought leadership and expert individual contribution for a team of Senior and Staff/Lead AI Engineers, reporting to a Director of AI Engineering.
  • Lead the development of next-generation recommender systems on top of foundational LLMs.
  • Design and train large language models (LLMs) from scratch or adapt existing models to achieve state-of-the-art performance on recommendation tasks.
  • Drive architectural decisions for foundational model development and deployment, ensuring scalability, efficiency, and robustness.
  • Provide technical leadership and mentorship to a team of engineers, fostering a culture of innovation and excellence.
  • Collaborate with cross-functional teams (product engineering, infrastructure) to identify high-impact opportunities and integrate models into new use cases across the LinkedIn ecosystem. 
  • Define and execute rigorous evaluation strategies to benchmark the performance of foundational models against state-of-the-art solutions.

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