Sr. Staff AI Engineer, AI Modeling Expert

Sr. Staff AI Engineer, AI Modeling Expert

At LinkedIn, our Core AI (Foundational AI) organization is dedicated to transforming the professional world through innovative solutions, including advanced models, agents, and AI systems. We aim to enhance the experiences of over a billion members worldwide, enabling them to connect, learn, and grow in unprecedented ways. We develop next generation AI technologies that understand the unique needs of professionals and proactively assist them in achieving their goals.

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: The Data Amplification team's vision is to build the most comprehensive professional identity in the world for LinkedIn members and companies. We combine LinkedIn's rich onsite data with sourced external data, then apply state-of-the-art techniques in large language models, reinforcement learning, and multi-modal inference to create a dynamic, continuously evolving view of the global workforce. This enhanced understanding empowers LinkedIn to deliver more relevant insights, connections, and opportunities, directly advancing our mission to create economic opportunity for every member of the global workforce. We are tackling some of LinkedIn's most challenging modeling problems through unstructured signal inference developing models that extract meaning from noisy, multi-modal professional data using LLMs, embeddings, and advanced entity linking techniques.

Responsibilities: Senior Technical Leader who will provide expert-level individual contributions to advanced models, in addition to thought leadership, mentorship, and strategy for a team of 10-15 engineers working on critical high-profile initiatives. Continuously improve recommendations and predictions through member feedback loops and RL-driven optimization to build adaptive systems that learn from member interactions. This work powers applied AI for member impact: inferring rich, dynamic representations of members and their networks; predicting credibility with high precision; assigning nuanced professional tags at scale for products like ads targeting or recruiter search; and generating high-quality candidate sets for ranking, recommendations, and notifications. These models are deployed end-to-end across LinkedIn's ecosystem, driving smarter feed recommendations, more relevant connection suggestions, and the next generation of semantic search. Our focus is on applied AI at scale, where every improvement directly shapes the professional experience for hundreds of millions of members. Lead the development of next-generation inferences, credibility modeling, tagging, and other solutions related to AI-driven personalization. Design and train large language models (LLMs), working with model distillation and compression for inference at scale. 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. Stay at the forefront of research in AI modeling and related fields, contributing to the broader research community through publications and presentations. Define and execute rigorous evaluation strategies to benchmark the performance of foundational models against state-of-the-art solutions.

Basic Qualifications: 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

Preferred Qualifications: 10+ years of overall industry/research experience in AI and/or Machine Learning. 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.

Suggested Skills: AI Modeling, Reinforcement Learning (RL), Technical Leadership, Large Language Models (LLMs)

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.

Compensation

LinkedIn is committed to fair and equitable compensation practices. The pay range for this role is $191,000 - $315,000. 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.