Who we are

At TwelveLabs, 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 $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. It is the differences in our cultural, educational, and life experiences that allow us to constantly challenge the status quo. We are looking for individuals who are motivated by our mission and eager to make an impact as we push the bounds of technology to transform the world. Join us as we revolutionize video understanding and multimodal AI.

About the team

The Field Engineering team partners with leading organizations in Media & Entertainment, Sports, and Advertising to deploy production-grade video AI systems. We operate at the intersection of customer delivery and core platform development, converting early deployments into repeatable system standards and evaluation practices that scale across commercial environments.

About the role

We are hiring a Forward Deployed Engineer (FDE) to push the frontier on what is possible with video AI across content discovery, automated editing, audience insights, monetization, and operational workflows by leading end-to-end deployments of our models inside Media & Entertainment, Sports, and Advertising organizations. You will embed with customers who are deep experts in their creative and operational domains, translating real-world video data, infrastructure constraints, and business requirements into production systems.

You will measure success through production adoption, measurable workflow impact, and evaluation-driven feedback loops that inform product and model roadmaps. You'll work closely with Product, Research, Sales, Partnerships, and Engineering to deliver scalable video AI systems that handle high-volume video workloads, meet security requirements, and integrate with existing media infrastructure.

This is a highly self-directed and creative role where you'll need to thrive in an unstructured, rapidly expanding and evolving environment.

This role is remote. Travel up to 40% is required.

In this role you will:

Design and ship production systems around our video foundation models, owning integrations with media infrastructure, video processing pipelines, data provenance, reliability, and on-call readiness across content workflows.

Lead discovery and scoping from pre-sales through deployment, translating ambiguous workflow needs into hypothesis-driven problem framing, system requirements, and an execution plan with measurable business outcomes.

Build in large-scale video environments where performance optimization, cost efficiency, security controls, and scalability shape architecture, operating procedures, and failure handling.

Run evaluation loops that measure model and system quality against workflow-specific benchmarks (e.g., search relevance, content accuracy, processing throughput) and use results to drive model and product improvements.

Define launch criteria including performance metrics, acceptance benchmarks, and success measures, driving delivery until sustained production impact is demonstrated.

Distill deployment learnings into hardened primitives, reference architectures, integration patterns, and benchmark harnesses that scale across Media & Entertainment customers.

You'll thrive in this role if you:

Bring 5+ years of software/ML engineering or technical deployment experience with customer-facing ownership in Media & Entertainment, Sports, Advertising, or video-centric industries.

Have owned customer AI/ML deployments end-to-end from scoping through production adoption, improving them through evaluation design, error analysis, and iterative refinement that tightens acceptance criteria over time.

Have deep understanding of video workflows, video workloads, and media infrastructure including video processing pipelines, content management systems, CDNs, and high-volume data architectures.

Possess strong infrastr