Principal AI Engineer, Search AI

LinkedIn•Mountain View, CA

6d•Hybrid

About The Position

As a Principal AI Engineer on LinkedIn Search, you will serve as the technical architect behind our shift from semantic search to Agentic Search. You will lead the technical vision and execution for multi-step reasoning, autonomous execution, tool/API orchestration, and conversational search workflows that understand complex intent, synthesize insights, and proactively take action on behalf of members. Working across ranking, infrastructure, and multiple partner teams, you will architect the AI systems that turn static search results into an adaptive and agent-driven intelligence engine for 1B+ members.

Requirements

7+ years of industry experience in software design, development, and algorithm related solutions.
5+ years in an Architect, Staff+, Principal, or equivalent technical leadership position.
Background in Machine Learning and Artificial Intelligence.
BS (or higher, e.g., MS, or PhD) in Computer Science or related technical field involving coding (e.g., physics or mathematics), or equivalent technical experience.

Nice To Haves

10+ years of overall experience including multiple years of high-level technical leadership, designing and implementing large-scale AI solutions related to ranking, retrieval, relevance, recommendations, personalization, and search.
Active participation in open-source projects related to search, recommendation, or NLP and/or contributions to conferences such as NeurIPS, ICML, ACL, or RecSys.
Hands-on understanding of agentic AI loops — how a model decides to call a tool / agent, how a result re-enters context, how loops terminate, and where they fail.
Able to decompose a problem from a first-principles perspective.
Able to communicate with equal ease with both engineers, product, and senior leadership.
Large Language Models (LLMs)
Agentic AI
AI Search & Recommendation Systems

Responsibilities

Design and scale multi-agent reasoning frameworks, tool-use protocols, and memory systems, optimized for real-time search workflows.
Build rigorous evaluation harnesses and guardrails for non-deterministic agent workflows, ensuring enterprise-grade trust, attribution, and safety at global scale.
Build runnable, state-of-the-art agentic POCs to prove out new paradigms (multimodal search, proactive task execution, autonomous candidate/job matching) and push them directly to production.
Set technical standards, engineering best practices, and architectural principles for AI/LLM deployment across the broader search organization.
Provide expert hands-on contribution.
Design multi-step reasoning frameworks, task planning systems, and tool-calling agents that solve complex operational or data challenges.
Define systems and standards for model evaluation, agent orchestration, and memory/context management.
Implement rigorous safety guardrails, error handling, logging, evaluation mechanisms, and fallback strategies for production LLMs.
Serve as a Technical Leader, mentoring engineers and guiding complex technical decisions.