Staff ML Research Engineer, Multimodal Structure & Marengo

🏢 Twelve Labs · 📍 Seoul, South Korea · 💼 hybrid · ⏰ FullTime

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Position Summary & Responsibilities

Who we are

Video is 90% of the world's data. Most of it is invisible to machines.

TwelveLabs builds the intelligence layer to change that. Our multimodal AI models understand video the way humans do — across sight, sound, and motion — and power production-scale AI workloads across media, entertainment, sports, security, and government.

We have raised more than $210 million from NEA, Radical Ventures, Amazon, NVIDIA, Snowflake, Databricks, Index Ventures, NAVER Ventures, Korea Investment Partners, Quadrille Capital, Red Bull Ventures, and AI pioneers including Fei-Fei Li, Silvio Savarese, and Alexandr Wang.

We are a global company, headquartered in San Francisco with offices in Seoul, New York, and London, and employees around the world. We believe the differences in our cultural, educational, and life experiences make our products stronger. Building technology that understands the world in all its complexity requires people who see it from every angle. We are looking for individuals who are driven by hard problems and want their work to matter. Come build it with us!

About Jockey

Jockey is TwelveLabs' unified agentic system that reasons across your videos and images. It combines a reasoning model with a memory layer that builds a knowledge store from your corpus.

No context window holds a video archive. We work at a million hours of video. A single model forward pass can tell you about one file; it can't reason across a corpus, and no context window closes that gap. Jockey decomposes a query, retrieves, segments, and reasons across thousands of videos and images. Point it at an archive, ask for a highlight reel or the best viral moments, and it returns timestamped cuts you can use. Corpus-level understanding you can act on is the whole product.

Built for agents, not just people. As AI agents increasingly become the primary consumers of video, we're building production-grade infrastructure that scales to millions of hours while delivering reliable, high-quality results for both human users and autonomous agents.

We build on models we own. Marengo, our embedding model, resolves a query l

🎯 Candidate Preparation Guide For Twelve Labs Applicants

Key Role Competencies & Evaluation Highlights

Key Role Focus:

State-of-the-art multimodal machine learning research capability, high-performance video understanding, and efficient model scaling.

📋 Preparation Priorities (Key Areas to Clarify):

1. Purely academic mindset with no interest in latency, compute budgets, or real-world product application.
2. Superficial understanding of modern deep learning frameworks and custom CUDA/kernel optimization.
3. Inability to explain complex neural network behavior simply and clearly.

✨ Recommended Role Alignment Hook:

I scale multimodal foundation models that truly understand video context, balancing state-of-the-art accuracy with production inference speeds.

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