Senior Machine Learning Engineer at Coody focusing on GenAI and fullstack projects. Collaborating across industries to deliver impactful machine learning solutions over extended assignments.
About the role
- 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.
Responsibilities
- 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
- 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
- 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
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