テックリードマネージャー, Jockey Core
オペレーション正社員リードソウルハイブリッド
Jockey Coreチームを立ち上げ、モデル選定からプロダクションサービングまでのロードマップを統括します。LLM推論の最適化経験が必須であり、実務を兼務するテックリードマネージャーとしてチームビルディングと技術的な意思決定を主導します。データに基づきレイテンシやコスト効率を判断する能力が求められます。修士・博士号やモデル圧縮の経験は歓迎されます。
Jockey Coreチームを立ち上げ、モデル選定からプロダクションサービングまでのロードマップを統括します。LLM推論の最適化経験が必須であり、実務を兼務するテックリードマネージャーとしてチームビルディングと技術的な意思決定を主導します。データに基づきレイテンシやコスト効率を判断する能力が求められます。修士・博士号やモデル圧縮の経験は歓迎されます。
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!
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.
The Cognition Models team owns the models that turn video into structured understanding and reasoning: Pegasus, our video-language model, and Jockey Core, the reasoning LLM behind Jockey. In the model stack we sit between Perception Models (embeddings and retrieval) and the agent system — taking what's retrieved and producing structured understanding and the reasoning to act on it.
We focus on multimodal systems with high instruction-following capability and complex, hierarchically structured outputs. Our work spans training infrastructure from pre-training to RL, temporal segmentation and structured metadata extraction, large-scale inference and serving systems, data-curation and evaluation pipelines, and building Jockey Core. We ship products with real-world value rather than doing research in isolation, working as a goal-oriented, cross-functional team of ML researchers and engineers — using the most advanced compute in the world, including NVIDIA B300s, to accelerate the research-to-production cycle.
Jockey Core is the reasoning LLM at the center of Jockey — the model that decomposes a query, decides what to retrieve and segment, and reasons over the results into an answer you can act on. It sits in the critical path of every agent step, so its quality, latency, and cost directly shape what Jockey can do. Jockey Core is a model we own and serve end to end, and we improve it continuously so Jockey's quality compounds with every release.
This is a Tech Lead Manager role to build and lead a newly founded team building Jockey Core — a leader who stays deeply hands-on while standing up and growing the team.
Build and lead the founding team — hiring, growth, delivery, and technical direction.
Own Jockey Core's end-to-end roadmap, from model and engine selection through model-efficiency work (pruning, quantization, distillation) to production serving and scale-out.
Stay hands-on: lead critical system and serving/inference architecture decisions, and set the technical bar through design review.
Judge latency/throughput/cost tradeoffs with measured data, and partner with the Pegasus, agent, and infrastructure teams on capacity and SLOs.
Explore and adopt AI-assisted development tools (Claude, Gemini, GPT) to raise team productivity.
A track record leading ML/infrastructure teams as a hands-on tech lead or manager — ideally founding or scaling a team from small.
Deep experience serving and optimizing large-scale LLM inference in production (vLLM, TensorRT-LLM, SGLang, or similar), across techniques like batching/scheduling, quantization, disaggregated prefill/decode, and speculative decoding.
A habit of driving ambiguous technical decisions with measured latency/throughput/cost data.
Excellent communication and people leadership.
Experience with model compression (pruning, quantization-aware training, distillation) or productionizing reasoning/agentic LLMs.
Experience with multi-region/multi-cluster serving or large-scale GPU capacity planning.
Contributions to or customization of an LLM inference server's internals.
A Master's/PhD in Machine Learning, Computer Science, or a related field.
글로벌 B2B 고객과 함께 성장하는 Global Team
자율성과 협업을 모두 갖춘 하이브리드 근무
최신 맥북 및 70만 원 상당 재택근무 장비 지원, 3년 주기로 최신 장비 교체
Tokens never sleep - Tech 직군 LLM 토큰 무제한 지원
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독감 예방접종비 지원
연말 2주간 유급 Holiday Break 운영