Chatbot Algorithm Engineer - Marketplace Intelligence and Data (2027 Graduate)

Design and develop algorithms for AI assistants and agentic applications, covering areas including LLM post-training, supervised fine-tuning (SFT), Mixture-of-Experts (MoE), and user preference alignment using reinforcement learning or preference optimization techniques such as DPO and GRPO. Build LLM-powered recommendation systems for user intent understanding and recommendation, including retrieval, coarse and fine ranking, user sequential modelling, semantic representation learning, and generative recommendation.

Develop production-ready LLM applications that integrate models with tools, APIs, knowledge bases, memory systems, and business workflows to solve real-world customer service and recommendation problems. Explore and implement advanced agent harness engineering techniques, including: Agent loop and graph orchestration Planning, reasoning, tool selection, and execution Multi-agent collaboration and task delegation Skill design, skill mining, and skill lifecycle management Context, memory, and knowledge management Reflection, feedback loops, and self-improvement mechanisms Evaluation, observability, failure analysis, and guardrail design Design and develop reusable algorithm platforms, evaluation frameworks, experimentation pipelines, and supporting tools to improve the development, deployment, and iteration efficiency of AI assistant capabilities.

Establish robust offline and online evaluation methodologies for AI assistants and agents, covering task success rate, response quality, tool-use accuracy, safety, latency, cost efficiency, user satisfaction, and business impact. Collaborate closely with product, business, operations, data, and engineering teams throughout the complete lifecycle of intelligent customer service products—from problem definition and solution design to development, deployment, performance monitoring, root-cause analysis, and continuous user-experience improvement.

Track emerging developments in LLMs, agents, recommendation systems, and AI engineering, and translate relevant research or industry best practices into practical solutions for production environments. Bachelor's degree or higher in Computer Science or a related field Proficiency in at least one programming language, such as Python, C++ & Go Skilled in at least one mainstream deep learning framework, like Py.

Torch, Tensor. Flow Passion for LLM-related technology and solving challenging real-world problems Deep understanding of computer fundamentals, including data structures, algorithms, and machine learning Familiarity with mainstream algorithmic models in large language models, natural language processing, recommendation, and multimodality (including principles and implementation) Prior research or internship experience in natural language Q&A or recommendation technology is preferred