Senior AI Engineer | Senior Technical Lead - AI Modeling

Senior Staff AI Engineer | Senior Technical Lead - AI Modeling Job Details

• Full-time
• Workplace Type:
  Hybrid

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.

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.

This role will be based in Sunnyvale, CA.

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.

• 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 Linked In ecosystem.
• Define and execute rigorous evaluation strategies to benchmark the performance of foundational models against state‑of‑the‑art solutions.

• 2+ years as a Technical Lead, Staff Engineer, Principal Engineer, or equivalent.
• 5+ years of industry experience in AI or Machine Learning Engineering.
• BA/BS Degree in Computer Science or related technical discipline or equivalent practical experience.

• 10+ years of overall industry (or industry + research) experience in AI and/or Machine Learning, including significant work with end‑to‑end solutions.
• PhD in Computer Science, Artificial Intelligence, Machine Learning, or a related field.
• Expert‑level understanding of deep learning architectures, particularly Transformer models, and experience training and fine‑tuning LLMs and applying them to recommender systems.
• Extensive experience developing models with advanced reasoning and planning capabilities.
• Strong programming skills in Python and relevant deep learning frameworks (e.g., PyTorch).
• Significant contributions to the field of AI, demonstrated through publications in top‑tier conferences (e.g., NeurIPS, ICLR, ICML, ACL) or impactful open‑source projects.
• Proven ability to build models that accurately interpret and follow complex, nuanced instructions (zero‑shot or few‑shot). Also, experience developing models that can evaluate their own progress, identify errors, and adjust their approach accordingly.
• Strong understanding of reinforcement learning (RL) techniques and their application to agent training in language‑based environments.
• Experience with specific techniques for improving reasoning and planning in LLMs: e.g., program…