Superintelligence Lab - Tech Lead (Agentic Search, Staff / Principal Level)
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Before you apply
LocationLG AI Research Magok, Gangseo-gu, Seoul, South Korea
Work modeon-site
Typefull-time
SeniorityLead
DepartmentSuperintelligence Lab
Company size501–1,000 people
PostedAug 23, 2022 · 4y ago
Verified live12h ago
May no longer be actively hiring
Posted by the company on Aug 23, 202274% of this company's open roles have been listed for 90+ days
At a glanceSummarised by Seekless from the posting.
Deep expertise across the full search stack, beyond dense retrieval
Experience building agentic systems, memory systems, or learned orchestrators
Applied RL or sequential decision-making to search, agents, or tool use
Built end-to-end evaluation systems combining offline evals, online experiments, and production telemetry
Scaled ML systems under demanding quality, latency, reliability, and cost constraints
Shipped research breakthroughs as reliable, production-grade capabilities
Led high-impact technical programs across multiple teams and disciplines
A first-principles mindset and strong Staff / Principal-level technical judgment
Skills
Python
RL
search systems
retrieval
agentic systems
orchestrators
evaluation systems
ML systems
About the Role
우리는 사용자의 의도를 이해하고, 필요한 정보를 탐색·검증·종합하여 신뢰할 수 있는 결과를 만들어내는 agentic search system을 구축하고 있습니다. 혁신적인 retriever 학습, agentic 시스템 설계, memory/context 최적화, orchestrator RL학습, evaluation, inference efficiency까지 전체 기술 방향과 프로젝트를 이끄는 Senior Tech Lead 포지션입니다.
Staff / Principal-level MTS로서 장기적인 비전과 기술 컴포넌트를 정의하고 여러 컴포넌트 사이를 정렬하기 위한 핵심 의사결정을 내릴 수 있어야 합니다. Research outcome을 production-grade 서비스로 발전시키고, 조직의 engineering 및 research standard를 성장시켜 차세대 Tech Lead들을 육성하는 책임도 있습니다.
What You’ll Do
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Set the technical vision and roadmap for end-to-end Agentic Search.
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Lead execution from foundational research through production deployment.
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Advance retrieval across sparse/dense/hybrid, reranking, and learning-to-retrieve systems.
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Co-design model–system interfaces spanning search, memory, planning, tool use, and orchestration.
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Develop RL and learning-based methods for search policies, routing, and orchestrator behavior.
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Build rigorous evals for retrieval, grounding, citations, tool use, and long-horizon task completion.
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Optimize quality, latency, throughput, and inference cost across the full search stack.
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Provide technical leadership across Research, Engineering, Infrastructure, and Product.
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Mentor senior technical talent and raise the bar for architecture, research, and execution.
You Might Thrive in This Role If You Have
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Deep expertise across the full search stack, beyond dense retrieval.
•
Experience building agentic systems, memory systems, or learned orchestrators.
•
Applied RL or sequential decision-making to search, agents, or tool use.
•
Built end-to-end evaluation systems combining offline evals, online experiments, and production telemetry.
•
Scaled ML systems under demanding quality, latency, reliability, and cost constraints.
•
Shipped research breakthroughs as reliable, production-grade capabilities.
•
Led high-impact technical programs across multiple teams and disciplines.
•
A first-principles mindset and strong Staff / Principal-level technical judgment.
전형 절차
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Application Review → Coding Test → Technical Interview (Online) → Culture Fit Interview (Onsite)
* 전형 절차는 변경될 수 있습니다. 서류 합격 시 전형 절차에 대해 별도로 안내 해 드립니다.