Senior Staff AI Engineer | Senior Technical Lead - AI Modeling

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 LinkedIn 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.

- Also, 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 synthesis, symbolic reasoning, neuro-symbolic AI.

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.

LinkedIn is committed to fair and equitable compensation practices. The pay range for this role is $241,000 - $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 applicable incentive compensation plans. For additional information, visit: https://careers.linkedin.com/benefits.

Please follow this link to access the document that provides transparency around the way in which LinkedIn handles personal data of employees and job applicants: https://legal.linkedin.com/candidate-portal. Basic

• 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.

• Also, 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 synthesis, symbolic reasoning, neuro-symbolic AI.

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.

LinkedIn is committed to fair and equitable compensation practices. The pay range for this role is $241,000 - $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 applicable incentive compensation plans. For additional information, visit: https://careers.linkedin.com/benefits. Location:

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