Job Description:
Our team aims to apply LLM/Agent technology to our recommendation system and Agent Auto Quality Assurance, thereby improving service satisfaction and efficiency for chatbots and agents. Your scope will include, but not be limited to:
- Generative RecSys: Design and develop core components of the chatbot's recommendation, adopting the thought of a generative model (e.g. LLM reasoning/scaling law) to improve CTR of RecSys.
- Proactive Support Agents: Modeling user's CS behaviour based on user profiles and historical interaction data to recognise users real intent and answer it proactively.
- Feature Insight Agents: Automatically reason potential features and defining user's event from Agent-Human dialogues.
- AutoQA Agent: Build an in-domain LLM with SFT for long context and design memory context management to check the agent service quality automatically.
Requirements:
- Currently pursuing a Bachelor's degree or above in Computer Science, Artificial Intelligence, or a related discipline.
- Solid experience with optimisation of end-to-end RecSys, including retrieval-ranking systems, Deep Learning RCMD (e.g., xDeepFM, DIN, MMoE), as well as LLM-based generative recommendation models (e.g., HSTU, OneRec, RankMixer).
- Proficient in Python and experienced with deep learning frameworks such as PyTorch or TensorFlow.
- Strong analytical and problem-solving abilities, with a passion for building intelligent, user-centric products.
- Experience in training and fine-tuning large language models using techniques such as supervised fine-tuning (SFT) or reinforcement learning (RL).
- Prior experience developing or optimising algorithms for large-scale recommendation, search, or advertising systems.
- Experience in chatbot algorithm development is a strong plus
