Senior Engineering Manager, Search - Cognition System

Seoul, South KoreaSenior7+ years

Who we are

At Twelve Labs, we are pioneering the development of cutting-edge multimodal foundation models that have the ability to comprehend videos just like humans do. Our models have redefined the standards in video-language modeling, empowering us with more intuitive and far-reaching capabilities, and fundamentally transforming the way we interact with and analyze various forms of media.

With a remarkable $107 million in Seed and Series A funding, our company is backed by top-tier venture capital firms such as NVIDIA’s NVentures, NEA, Radical Ventures, and Index Ventures, and prominent AI visionaries and founders such as Fei-Fei Li, Silvio Savarese, Alexandr Wang and more. Headquartered in San Francisco, with an influential APAC presence in Seoul, our global footprint underscores our commitment to driving worldwide innovation.

We are a global company that values the uniqueness of each person's journey. If you're a zero-to-one achiever, a ferocious learner, and a kind team player who motivates others, you'll find a home at TwelveLabs. Even if you don't meet every qualification listed, we encourage you to apply.

About the Team

트웰브랩스의 검색(Search) 시스템 전반을 책임지는 팀입니다. 검색·리트리벌 플랫폼(retrieval platform)부터 에이전틱 검색(agentic search) 레이어까지, 사람과 에이전트가 영상을 탐색하는 모든 경로의 근간을 만듭니다.

Full-stack 검색 시스템: 트웰브랩스는 세계 최고의 영상 임베딩 모델(Marengo)과 영상-언어 모델(Pegasus)을 기반으로 '영상 검색'을 대표하는 회사로 자리매김해 왔습니다. 키워드 기반 검색부터 임베딩 검색, 에이전틱 검색 시스템까지 비디오·멀티모달에 필요한 풀 스택 검색 시스템(Full-stack Video&Multimodal Search System)을 구축하고자 합니다.

사람과 에이전트를 위한 검색: 트웰브랩스는 검색을 호출하는 주체가 사람보다 에이전트가 더 많아질 것으로 예상합니다. 이 팀은 사람과 에이전트 모두를 위한, 수백만 시간 규모의 영상에서도 안정적으로 동작하는 프로덕션 수준의 검색 시스템과 에이전틱 검색 하네스(Agentic Search Harness)를 만듭니다.

글로벌 임팩트: 연구 결과가 짧은 사이클 안에 전 세계 고객에게 전달되는 환경에서 기술적 임팩트를 만들어갑니다.

About the Role

As the Senior Engineering Manager, Search for our Cognition System, you will own and lead the team that builds search at TwelveLabs end to end, This includes both the core search & retrieval platform and the agentic search harness on top of it. Your goal is not to build a cool demo, but a production system that ingests millions of hours of video and serves queries at very high RPS.

We're looking for a technical leader who understands:

The complexity and engineering rigor required to stand up a production-grade search system that scales to millions of hours across both ingestion and index creation.
The agentic search harness that reasons about user intent (human and agent) across millions of videos to surface what the user is truly asking for.

You are also a people manager who leads both senior and junior engineers and scientists. You are not a day-to-day IC, but you read traces, review code, and debate architecture as a peer and you make sure the system works in production, not just in a demo.

In this role, you will

Own the search system end-to-end (e.g., vector/ANN indexing, lexical retrieval, hybrid fusion, reranking, and temporal (segment-level) search), built on Marengo embeddings and Pegasus and scaled to millions of hours across both ingestion and index creation.
Design and build the agentic search harness that infers human and agent intent and orchestrates multi-turn, session-based (conversational), and parallel search sessions, including subagents that can be invoked by a primary agent.
Set and own the search-quality bar for both human- and agent-initiated search, and drive continuous improvement against them.
Own reliability and systems design for the search stack so the system stays dependable as both human and agent traffic grows.
Drive cross-functional alignment with Research/Training, Platform/Infra, and Product on retrieval strategy, API contracts, and system boundaries.
Build and lead the team: recruit strong engineers and scientists, set engineering patterns and standards across the search stack, and raise the bar through architecture review, code review, and technical mentorship.

You may be a good fit if you have

7+ years building production ML or search systems, with deep experience in search, retrieval, or recommendation at scale.
Engineering leadership where you've built and led teams of engineers (and scientists), and you can attract, hire, and grow senior ICs.
Deep experience with information retrieval systems (e.g. embedding-based search, lexical search (BM25 / OpenSearch / Elasticsearch), hybrid retrieval, reranking, and score normalization) at production scale.
Strong system-design instincts across ML infrastructure, distributed systems, and developer-facing platforms.

Preferred Qualifications

Experience building agentic search systems
Experience with multimodal or video search/retrieval
Familiarity with knowledge graph construction, entity resolution, or graph-backed retrieval.
Good English communication skills, verbal and written.