Algorithm Engineer, TikTok E-Commerce (Conversational AI)

Tiktok-posted 4 months ago
San Jose, CA
5,001-10,000 employees
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The future of e-commerce customer service is intelligent, efficient, and AI-driven. Our team is dedicated to replacing traditional human-agent customer service with an advanced AI-powered conversational system that provides instant, intelligent, and seamless support for TikTok's global e-commerce platform. By leveraging Large Language Models (LLMs) and NLP, we are building an AI customer service system that can understand user queries, resolve disputes, guide transactions, and enhance the overall shopping experience without the need for human intervention. Our cutting-edge AI is designed to handle complex customer interactions, including answering product inquiries, resolving order issues, processing refunds, and assisting sellers with operational tasks. Through LLM post-training, we ensure that our AI assistant is continuously learning and improving, providing more accurate, context-aware, and human-like interactions. By joining us, you will be at the forefront of transforming customer service in e-commerce, helping build an AI system that understands, adapts, and provides intelligent solutions-all while reducing costs and improving efficiency for merchants and the platform.

  • Develop AI-Powered Customer Service Systems: Design and implement an AI-driven conversational customer service agent that can handle e-commerce inquiries, complaints, refunds, dispute resolutions, and logistical issues, replacing traditional human customer service agents.
  • LLM Post-Training & Data-Efficient Learning: Apply state-of-the-art LLM post-training techniques, such as instruction tuning, reinforcement learning from human feedback (RLHF), and continual learning, to optimize AI customer service responses with minimal labeled data.
  • Benchmark and training data construction: Identify challenging customer service interactions, such as policy clarifications, dispute handling, and multi-turn complaint resolution, and construct specialized datasets to enhance AI training.
  • Develop Multilingual Customer Support: Build AI models capable of handling customer service interactions across multiple languages and cultural contexts, ensuring accurate translation and appropriate responses for a diverse global audience.
  • Optimize Model Efficiency & Deployment: Work on model compression, quantization, and efficient inference techniques to ensure the AI customer service assistant can run at scale with low latency and high reliability.
  • Enhance LLM-Based Customer Interaction Models: Implement LLM post-training strategies to improve customer support interactions, reducing errors, hallucinations, and irrelevant responses.
  • Create Automated Dispute Resolution & Policy-Adaptive AI: Develop intelligent models capable of handling disputes, verifying transaction details, and ensuring platform compliance in automated responses.
  • Refine Response Evaluation Metrics: Define and implement quality evaluation metrics for AI-generated responses to track customer satisfaction and improve conversational AI quality through A/B testing and iterative optimization.
  • Enable AI-Seller Collaboration: Build AI-powered seller assistance tools to help merchants quickly respond to customer inquiries, manage store operations, and resolve disputes efficiently.
  • Optimize Large-Scale Model Deployment: Work on model compression, inference optimization, and edge AI deployment to ensure real-time, high-quality customer service experiences at scale.
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