Staff Machine Learning Engineer

Twelve Labs

Staff Machine Learning Engineer responsible for leading ML engineering in video AI product development. Mentoring team members and designing scalable production systems for advanced video language models.

Posted 4/13/2026full-timeSeoul • 🇰🇷 South KoreaLeadWebsite

Tech Stack

Tools & technologies

Kubernetes

About the role

Key responsibilities & impact

Drive technical direction for ML engineering within Pegasus while remaining deeply hands-on in critical system design and implementation.
Own the design and evolution of critical production ML systems for Pegasus, with a focus on scalability, reliability, performance, and fast iteration.
Lead technical decision-making across model deployment, inference architecture, metadata systems, and ML infrastructure for Video Language Models (VLMs).
Improve and automate the end-to-end ML lifecycle so research advances can translate into product improvements quickly and reliably.
Mentor engineers and raise the team’s execution bar through strong technical judgment, design reviews, and hands-on collaboration.
Explore and adopt AI-assisted development tools such as Claude, Gemini, and GPT to improve productivity across coding, experimentation, debugging, and documentation.

Requirements

What you’ll need

Significant experience building and productionizing ML systems as a hands-on individual contributor.
Experience driving technical direction across ML projects and making architectural decisions in complex production environments.
Strong foundations in machine learning and deep experience with multimodal systems such as vision, language, or video-based models.
Experience building and evolving distributed ML or data workflows, ideally in Kubernetes-based environments.
Strong technical judgment across system design, performance, reliability, and long-term maintainability.
A track record of mentoring engineers and creating technical leverage beyond your own individual contributions.
Preferred qualifications include experience serving or optimizing LLM/VLM systems in production, including inference optimization, throughput and latency tuning, batching, caching, or quantization.
Experience designing and operating mission-critical AI/ML applications from 0 to 1 and scaling them in production.
Experience with large-scale training or serving infrastructure for ML systems, including high-performance GPU environments.
Master’s or PhD in Machine Learning, Computer Science, or a related technical field.

Benefits

Comp & perks

글로벌 B2B 고객과 함께 성장하는 Global Team
자율성과 협업을 모두 갖춘 하이브리드 근무
전 직원에게 맥북 및 70만 원 상당 재택근무 장비 지원, 3년 주기로 최신 장비 교체
식사·교통비 등 자유롭게 사용할 수 있는 월 60만 원 한도 법인카드 제공
사무실 내 스낵바(간식, 커피, 신선식품 제공)
연말 2주간 겨울방학 운영
연 1회 건강검진 지원

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills & Tools

machine learningmultimodal systemsdistributed ML workflowsinference optimizationthroughput tuninglatency tuningbatchingcachingquantizationhigh-performance GPU environments

Soft Skills

technical judgmentmentoringcollaborationdesign reviews

Certifications

Master’s in Machine LearningPhD in Machine LearningPhD in Computer Science