Senior ML Research Engineer, Multimodal Structure & Marengo

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 like "the moment we almost missed the flight" into real retrieval. Pegasus, our video-language model, returns structured, timestamped moments on a schema you define. We ship and improve both continuously, so Jockey's quality compounds with every release — no re-integration for customers. Few teams get to build an agent on a stack they control end to end. Deep expertise, one system, open culture. Foundation models, knowledge construction, search, and the agent harness all live in one org. Each team owns its domain and is expected to have deep expertise in it — but like a Formula 1 team, we optimize for the global system, not local parts. A model gain that doesn't expand what the agent can do isn't a gain. We trace a single algorithm change through to end-system behavior, and share work in progress weekly, not just finished results. Anyone can pull the context they need from any team. ABOUT THE TEAM This team develops two core capabilities for multimodal understanding: structure and semantics. Structure organizes video, audio, text, and documents into addressable units and models the relationships among them. Marengo, TwelveLabs' multimodal embedding model, represents what those units mean in a shared embedding space for understanding and retrieval. End-to-end model development: We work across a broad range of research areas, including video segmentation and tracking, temporal and hierarchical modeling, contrastive learning, and multimodal representation learning. The team owns the entire model development lifecycle, from building large-scale training datasets and designing model architectures to optimizing distributed training and developing robust evaluation frameworks. Research at scale: With access to world-class compute infrastructure, including NVIDIA B300 GPUs, we rapidly iterate on large-scale experiments, enabling fast progress on ambitious research problems. Research with real-world impact: The path from research to production is exceptionally short. We work closely with the Agent, Search, Product, and Infrastructure teams to continuously improve the models that power multimodal search and understanding for thousands of customers worldwide. ABOUT THE ROLE As a Staff ML Research Engineer working across multimodal structure and embeddings, you will set the technical direction for TwelveLabs' next-generation models and own the end-to-end development process, from research strategy and data architecture to training systems, production model APIs, and evaluation frameworks. This is a high-autonomy role at the intersection of video understanding, multimodal representation learning, large-scale systems design, and cross-team technical leadership. We're looking for someone who thrives in ambiguity: someone who can identify the highest-impact research problems, define the technical approach, an