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Twelve Labs is building AI infrastructure to enable machines to understand video at scale. Video comprises 90% of global data, yet most remains inaccessible to machine understanding. The company has developed multimodal AI models that analyze audiovisual information with sophistication exceeding human capability, applying this technology across media & entertainment, sports, security, and public sectors. Twelve Labs has raised over $300M from global investors including NEA, Radical Ventures, Amazon, NVIDIA, Snowflake, and others, with advisors including Fei-Fei Li, Silvio Savarese, and Alexandr Wang. The company operates globally from San Francisco, Seoul, New York, and London, with core R&D centered in Seoul.
The Infrastructure team believes data quality determines AI model performance. They build end-to-end high-quality datasets for training and evaluating multimodal AI models, collecting and processing video, image, and audio data, designing training datasets that unlock new model capabilities, and creating evaluation datasets reflecting real user experience. The team also develops and continuously improves internal tools to execute these processes efficiently.
As a Senior Infrastructure Engineer, you will design and build core infrastructure to operate the AI SaaS platform reliably and at scale. You will architect systems across diverse cloud environments (AWS, GCP, Azure) and on-premises deployments, supporting the video AI foundation models. Working in a fast-moving startup environment, you will optimize for performance, security, and flexibility while collaborating closely with multiple internal teams.
Key responsibilities include: designing and operating multitenant architecture for global enterprise customers; developing scalable CI/CD pipelines using Terraform; building flexible infrastructure spanning multiple cloud providers and on-premises environments; implementing advanced monitoring and security systems; designing extensible architecture to rapidly support new video AI models and services; and collaborating with product, engineering, and research teams to bring AI products to market.
Requirements: Experience building and operating infrastructure in AWS, GCP, or Azure; hands-on experience with Infrastructure as Code tools (Terraform, Ansible); Kubernetes and container-based workload operations; scripting and automation using Python, Go, TypeScript, or similar languages; CI/CD pipeline design and operations experience.
Preferred qualifications: 8+ years as an infrastructure engineer; multitenant SaaS architecture design and operations in enterprise environments; security and compliance-focused infrastructure architecture with audit experience; advanced monitoring and logging system implementation; simultaneous optimization for performance and cost efficiency; English communication ability.
