Senior Engineering Manager, Search - Cognition System

Twelve Labs Inc.

Job Overview

Location

Seoul, South Korea

Job Type

Full-time

Full Job Description

📋 Description

• Lead the end-to-end development of Twelve Labs’ production-grade search and retrieval system, scaling it to handle millions of hours of video across ingestion, indexing, and query serving at high RPS.
• Own the architecture and execution of both core search components (vector/ANN indexing, lexical retrieval via BM25/OpenSearch/Elasticsearch, hybrid fusion, reranking, and temporal segment-level search) built on Marengo embeddings and Pegasus models.
• Design and deploy the agentic search harness that infers intent from both human users and AI agents, enabling multi-turn, session-based, and parallel search workflows with subagent invocation capabilities.
• Establish and enforce the search quality bar for human- and agent-initiated queries, driving continuous improvement through metrics, evaluation frameworks, and user feedback loops.
• Ensure system reliability and scalability as traffic grows, implementing robust monitoring, fault tolerance, and performance optimization across the entire search stack.
• Drive cross-functional alignment with Research/Training, Platform/Infra, and Product teams on retrieval strategy, API contracts, and system boundaries to ensure cohesive product delivery.
• Recruit, hire, and retain top-tier engineers and scientists, building a high-performing team with a mix of senior and junior talent.
• Set engineering standards and patterns across the search stack through architecture reviews, code reviews, and technical mentorship, raising the team’s overall technical bar.
• Act as a technical peer: read system traces, review code, and participate in deep architectural debates — while remaining focused on production system stability over demo-level prototypes.
• Translate research innovations from the Cognition team into scalable, production-ready search features that deliver real-world impact to global B2B customers.
• Maintain a deep understanding of multimodal video retrieval challenges, including temporal reasoning, cross-modal alignment, and large-scale embedding storage and retrieval.
• Foster a culture of ownership, learning, and collaboration within the search team, encouraging innovation while maintaining rigorous engineering discipline.
• Balance long-term system architecture goals with short-term product delivery needs, ensuring the search platform evolves to support both current and future use cases.
• Communicate technical trade-offs and progress clearly to stakeholders across engineering, product, and leadership.
• Contribute to the global growth of Twelve Labs by enabling search capabilities that serve customers worldwide with low latency and high accuracy.

🎯 Requirements

• 7+ years building production ML or search systems with deep experience in search, retrieval, or recommendation at scale
• Proven engineering leadership experience building and managing teams of engineers and scientists
• Deep hands-on experience with information retrieval systems including embedding-based search, lexical search (BM25/OpenSearch/Elasticsearch), hybrid retrieval, reranking, and score normalization

🏖️ Benefits

• Hybrid work model combining autonomy and collaboration
• Monthly corporate card with 600,000 KRW limit for meals, transportation, and other expenses
• MacBook and up to 700,000 KRW worth of home office equipment provided to all employees, replaced every 3 years
• On-site snack bar offering snacks, coffee, and fresh food
• Two-week winter break at year-end
• Annual health checkup support
• English language education program support

Skills & Technologies

Elasticsearch

Senior

Hybrid

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Twelve Labs Inc.

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About Twelve Labs Inc.

Twelve Labs builds multimodal video understanding AI. Its cloud platform transforms long-form video into vector embeddings that capture visual, audio, speech and contextual information, enabling semantic search, summarization, chaptering, moderation and analytics through a single API. Developers upload video, index it, then query in natural language or image to retrieve exact moments, generate highlights or detect unwanted content. Models are pretrained on large-scale web video, continually fine-tuned for accuracy and latency, and deployable on dedicated GPU clusters for enterprise security. Founded in 2021, the San Francisco company serves media, ed-tech, safety and e-commerce customers worldwide.

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