Staff Research Scientist/Engineer - Embedding & Search at TwelveLabs

Seoul, , South Korea -

Full Time

Start Date

Immediate

Expiry Date

15 Mar, 26

Salary

0.0

Posted On

15 Dec, 25

Experience

2 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Video-Text Retrieval, Contrastive Learning, Temporal Video Understanding, Segmentation, Learning-To-Rank, Neural Reranking, User Modeling, Behavioral Analysis, Python, PyTorch, Large-Scale Model Training, Distributed Systems, ML Systems Deployment, Research, Experiment Design, System Optimization

Industry

Software Development

Description

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 a $110+ 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 Embedding & Search team sits at the core of TwelveLabs' video understanding capabilities. We design unified embedding spaces that span video, audio, text, and other modalities, and build retrieval systems that accurately surface results matching user intent across massive video collections. Our research spans a broad range of challenges: multimodal representation learning through contrastive and probabilistic approaches, temporal video understanding including hierarchical segmentation and boundary detection, neural ranking architectures for multi-stage retrieval, and user behavior modeling to understand how people actually search for and interact with video content. We care deeply about both the algorithmic innovations that push state-of-the-art and the human-centered insights that make our systems genuinely useful. About the Role This position spans two tracks—Research Scientist and Research Engineer—determined by your strengths and interests. These roles exist on a spectrum rather than as discrete categories; both contribute to research and implementation. As a Research Scientist, you will define research problems, formulate hypotheses, and design experiments that push the boundaries of what's possible in video search. You'll analyze user behavior patterns to uncover insights about how people interact with video content, and translate those insights into system improvements. You'll explore optimal architectures through rigorous ablation studies, develop evaluation methodologies that capture what matters, and communicate findings clearly to shape our technical direction. As a Research Engineer, you will translate research ideas into stable, reproducible systems. You'll design and optimize training pipelines for large-scale distributed environments, build experiment infrastructure that accelerates research cycles, and ensure our models perform reliably in production. Your work bridges the gap between promising research results and systems that serve thousands of customers worldwide. You might be a great fit if you have We're looking for candidates with research experience in areas that align with our mission: video-text retrieval and contrastive learning, temporal video understanding and segmentation, learning-to-rank and neural reranking, or user modeling and behavioral analysis. Your experience should be demonstrated through past projects, concrete contributions, and research outputs. You should be capable of independently driving research from ideation to execution. Strong proficiency in Python and PyTorch is essential, as is the ability to communicate effectively with colleagues from diverse backgrounds. Experience with large-scale model training, distributed systems, or deploying ML systems in production is a significant plus. We evaluate based on relevant technical skills and research experience rather than degrees alone, though this is typically supported by an MS/PhD or equivalent practical experience in a relevant field. What makes this role unique The gap between research and production is remarkably short here. Models and systems you build will be used by thousands of companies worldwide within months. We work as a unified team toward the broader goal of video search, rather than solving isolated problems. Our research philosophy balances rigorous experimentation with real-world application—we aim to build multimodal systems that are powerful, trustworthy, and genuinely useful. Others Work Location: Seoul Itaewon office + Pangyo satellite office Additional Info: 전문연구요원 편입/전직 가능합니다. Even if you don't check every box, we encourage you to apply. If you're a zero-to-one achiever, a ferocious learner, and a kind team player who motivates others, you'll find a home at TwelveLabs. Hiring Process Application Review → Recruiter Interview (비대면/30분) → Hiring Manager Interview (비대면/30분) → Technical Interview Round 1 (대면/60분) → Technical Interview Round 2 (비대면/90분) → Final Round Interview (비대면/30분) → Reference Check → Offer

Responsibilities

The role involves defining research problems, formulating hypotheses, and designing experiments to enhance video search capabilities. Additionally, it includes translating research ideas into stable systems and optimizing training pipelines for large-scale environments.