About the job
Who We Are:
TwelveLabs is at the forefront of developing innovative multimodal foundation models that enable video comprehension akin to human understanding. Our groundbreaking models have set new benchmarks in video-language modeling, enhancing our capabilities and revolutionizing how we engage with and analyze diverse media formats.
With an impressive $107 million in Seed and Series A funding, we're supported by premier venture capital firms including NVIDIA’s NVentures, NEA, Radical Ventures, and Index Ventures, alongside influential AI pioneers like Fei-Fei Li, Silvio Savarese, and Alexandr Wang. Our headquarters in San Francisco, complemented by a significant presence in Seoul, highlights our dedication to fostering global innovation.
We celebrate the individuality of every team member’s journey, believing that the diverse cultural, educational, and life experiences of our employees fuel our ability to challenge the status quo. We seek passionate individuals who resonate with our mission and are eager to make a significant impact as we advance technology to reshape the world. Join us in redefining video understanding and multimodal AI.
About the Role
As a Senior Staff Infrastructure Engineer at TwelveLabs, you will leverage your technical expertise and leadership skills to construct the systems that drive our multimodal foundation models. Your focus will be on designing and enhancing a scalable, secure, and high-performance infrastructure that accommodates extensive AI workloads across both cloud-based and on-premises environments.
This position demands strong technical acumen, an eagerness to delve into low-level systems when necessary, and the capability to influence infrastructure strategy through hands-on contributions and operational improvements. Your impact will be felt through your technical expertise and the results you deliver, rather than through hierarchical status, in a dynamic and fast-paced environment.
In this role, you will:
- Architect and advance cloud and hybrid infrastructure, blending hands-on execution with technical leadership.
- Guide the development of AI/ML infrastructure components, engaging directly in critical tasks when necessary.
- Define infrastructure standards and abstractions while maintaining close interaction with production systems.
- Collaborate closely with Machine Learning Engineers, Data Scientists, Backend Developers, and other key stakeholders to ensure system alignment and efficiency.

