About the TeamAt TikTok, we are passionate about building a world-class support experience for every user. Our team creates and powers the all-in-one customer support platform for TikTok and international business teams, with a focus on intelligent customer service solutions.We sit at the center of the user support journey, making sure that whenever someone needs help, they receive fast, accurate, and meaningful assistance. Our work is driven by three core priorities:
- Easy Access to Support: We design streamlined help channels that make it simple and intuitive for users to find the support they need, minimizing wait times and enhancing satisfaction.
- Precise Ticket Routing: Through intelligent categorization and routing systems, we ensure user inquiries reach the right teams quickly, improving resolution efficiency and user outcomes.
- Empowering Internal Teams: We build smart, reliable platforms and tools that equip our support teams to deliver professional, efficient, and user-centered service.
We are seeking an AI/NLP Operations Specialist to optimize and enhance our AI-driven customer service platform. You will be responsible for improving model accuracy in feedback recognition and routing, ensuring inquiries are efficiently distributed to the right support teams. This role requires deep business understanding, collaboration with AI/algorithm teams, and data-driven optimization to enhance overall system performance.
In order to enhance collaboration and cross-functional partnerships, among other things, at this time, our organization follows a hybrid work schedule that requires employees to work in the office 3 days a week, or as directed by their manager/department. We regularly review our hybrid work model, and the specific requirements may change at any time.
Responsibilities
1. Model Optimization and Strategy:
- Partner closely with algorithm teams to refine NLP models, improving intent recognition accuracy and routing efficiency
- Define optimization strategies based on business needs and model performance metrics
- Design and maintain a business label structure to enhance model training and classification quality
2. Data and Quality Assurance:
- Collaborate with customer service teams to design and implement high-quality data labeling processes
- Conduct regular quality reviews of model outputs, validating results and driving continuous improvement initiatives
3. Data Analysis and Monitoring:
- Build and maintain data monitoring systems to track model performance, detect anomalies, and identify root causes
- Analyze performance trends and use data-driven insights to propose actionable optimizations for both models and operational workflows
4. Cross-team Collaboration and Communication:
- Act as a bridge between business and algorithm teams, ensuring technical solutions are aligned with operational goals
- Communicate complex AI concepts and project updates clearly and effectively to non-technical stakeholders
