Senior Staff AI Engineer - Enterprise AI (Agentic AI)

LinkedIn•Mountain View, CA

2d•Hybrid

About The Position

LinkedIn's Enterprise AI team creates meaningful impact by connecting individuals to opportunities. The team works on a wide range of technical solutions including LLM fine-tuning, content generation, LLM-as-a-judge, prompt engineering, embedding-based retrieval, and search/ranking. The team has an ambitious roadmap and partners closely with Core AI (Foundational AI), Product, and other teams to deliver scalable, modern AI solutions with global member impact. The key focus is our agentic Hiring Assistant or LiHA product which is one of the top products for LinkedIn in 2026. If you’re looking to lead a highly visible, fast-moving, and exceptionally talented team at the intersection of cutting-edge innovation and real-world impact—and have fun while doing it— Enterprise AI is the place for you!

Requirements

Bachelor’s degree in Computer Science or related technical field or equivalent technical experience
7+ years of industry experience with large-scale recommendation systems

Nice To Haves

4+ years of technical leadership (Staff+) experience, including recent experience at the Senior Staff / L7 / Principal Engineer level.
MS or Ph.D. in Computer Science or related technical discipline
Experience designing agentic architecture, framework, and/or orchestration systems.
Strong understanding of reinforcement learning (RL) techniques and their application to agent training in language-based environments.
Experience with specific techniques for improving LLM post-training: e.g., program synthesis, symbolic reasoning, or neuro-symbolic AI.

Responsibilities

Provide expert thought leadership, technical guidance, and direct contribution for a team of 15-25 engineers working on high-impact initiatives for one of LinkedIn's top products.
Lead the design of conversational and agentic AI systems that perform functions related to talent acquisition.
Build and scale production-grade LLM agents that collect structured candidate signals, answer questions, and orchestrate multi-step, tool-using workflows with memory and reasoning.
Develop automated screening and interview agents to accelerate qualification, improve signal quality, and reduce time-to-interview while ensuring fairness and compliance.
Define technical direction and impact metrics, connecting conversational quality to downstream hiring outcomes and leading experimentation in enterprise-scale environments.
Evaluate and drive key engineering decisions such as whether to fine tune models or leverage off the shelf, how to best implement RAG, etc.
Collaborate with many talented AI Engineers, leaders, and cross-functional partners
Contribute to solutions as an expert technical practitioner