Jobs at TwelveLabs

Own and build GTM systems and automations across HubSpot, Clay, and partner integrations. Design quote-to-cash flows, maintain AWS co-sell pipeline, create multi-system workflows, ensure data quality and reporting, prototype and iterate GTM infrastructure, and collaborate with Sales, Growth, and Partnerships leadership.

Senior HRBP partnering with technical leaders in Korea to lead org design, workforce planning, talent management, performance reviews, promotions, calibration, succession, manager coaching, employee relations compliant with Korean law, and building HR playbooks and people-data driven programs across Research, Engineering, and Product.

Build and operate production machine learning systems for Pegasus, TwelveLabs’ video-language model. Responsibilities include ML deployment, inference, evaluation, monitoring, infrastructure, multimodal data processing, metadata serving, performance optimization, and reliability improvements. The role requires strong ownership, technical judgment, and collaboration across research and engineering to deliver scalable VLM systems.

Build and own internal backend products for model evaluation, regression testing, dataset discovery, and agentic context curation. Unify internal data and evaluation systems, enable secure, queryable access to petabyte-scale data across clouds with PII-aware access controls, integrate product usage signals, and harden internal agents for org-wide use.

Support the CEO and executive team by managing complex calendars, international travel, meeting and board materials, stakeholder communications, confidential information, and shifting priorities. Coordinate offsites, retreats, interviews, and executive logistics across global offices in the US, UK, and Korea. The role requires sound judgment, discretion, adaptability, strong communication, and the ability to support multiple senior executives in a fast-paced technology company.

Leads the engineering organization responsible for deploying and integrating TwelveLabs technology across cloud, private cloud, on-premise, and customer-controlled environments. Owns deployment automation, enterprise architecture, CI/CD, observability, security, and operational standards while converting bespoke customer requirements into scalable platform capabilities. Partners with customers, Product, Engineering, Sales, and Security, and builds a globally distributed Deployment and Integrations Engineering team.

The role involves driving research on complex problems in video analysis, designing experiments, collaborating with engineers, and translating research into production systems.

Lead and grow the team building Twelve Labs' production-grade search stack: vector/ANN and lexical indexing, hybrid retrieval, reranking, temporal segment search, and an agentic search harness that orchestrates multi-turn, session-based queries across millions of hours of video. Own reliability, retrieval quality metrics, cross-functional alignment, hiring, and technical standards.

Own TwelveLabs’ business systems architecture, integrations, master data standards, and analytics layer. Build trusted Databricks reporting for metrics such as ARR, burn, runway, headcount, and margins. Lead system evaluations and implementations, automate workflows, improve cross-functional data quality, and establish documentation and operating standards. Partner with Finance, GTM, People, and Engineering while serving as the foundation for a future business systems team.

Own the product roadmap and execution for co-development, distribution, and integrations with major cloud and data platforms. Scope and validate integrations, drive end-to-end technical and GTM launches, align cross-functional teams, and measure partner-sourced pipeline, usage, and adoption.

Own the agent-facing integration layer (MCP server) that exposes multimodal video/image AI capabilities to developers and agents. Build auth (OAuth/OIDC/RBAC), multi-tenant isolation, usage tracking, rate limiting, and reliability for enterprise scale. Design API contracts, handle failure modes, and partner with research to productize video understanding features.

Lead research on spatiotemporal and multimodal video representations, design training objectives, datasets, evaluations, and large-scale experiments, integrate task-specific vision signals, and collaborate with engineering to scale and productionize foundation-model capabilities across videos, clips, regions, and entities.

Lead and deliver multiple complex technical programs across platform infrastructure, model pipelines, and products. Define scope, dependencies, risks, and milestones; structure trade-offs; accelerate cross-team decisions; manage escalations; and align distributed KR/US teams to ensure timely execution.