AI Annotation Model Operations Specialist, Data Annotation Lab - AI Data Service and Operations

Responsibilities

Our Company will be prioritizing applicants who have a current right to work in Singapore, and do not require Our Company's sponsorship of a visa.

The AI Data Service and Operations (ADSO) team is responsible for providing safety and non-safety data annotation services and search operation services for all of the company's international products, which can also help international products build their own data ecological security.

- Automated Annotation Performance Monitoring & Issue Analysis. Own the monitoring and day-to-day operations of automated annotation projects. Track key performance metrics, conduct case studies and Root Cause Analysis (RCA) based on validation sets, sampled data, and online results, identify critical issues affecting model performance and stability, and drive issues to closure.
- Annotation Agent Workflow & Strategy Optimization. Develop a deep understanding of the end-to-end automated annotation Agent workflow. Identify gaps in strategy design, propose actionable optimization plans, and collaborate with relevant teams to complete experimental validation and strategy iteration.
- Experiment Design & Effectiveness Evaluation. Collaborate with Data Science, Algorithms, and other cross-functional teams to participate in experiment design and analysis. Define evaluation metrics and analytical approaches, support structured interpretation of experiment results, and facilitate data-driven problem solving.
- Cross-Project Knowledge Reuse & Methodology Building. Leverage hands-on experience from multiple automated annotation projects to identify common challenges and solution patterns across different business scenarios, and promote standardization and reuse of workflows, analytical methods, or operational mechanisms.

Qualifications

Minimum Qualifications
- Educational background in Statistics, Computer Science, Artificial Intelligence, or related field.
- Minimum of 3 years of experience in AI, large language models (LLM), or automated systems, with hands-on involvement in operations, evaluation, or production deployment.
- Solid understanding of model capabilities, limitations, and real-world application scenarios.
- Strong data sensitivity and analytical skills, with the ability to analyze metric trends, break down problems, and support judgments and decisions with data; basic understanding of experimentation and effectiveness evaluation.
- Ability to understand common technical concepts and analytical conclusions, and to communicate effectively with Algorithm, Engineering, and Product teams.