Machine Learning Engineer - AI Search (ByteDance Singapore)

关于这个机会

About the team Our Search Team focuses on LLM application algorithms, with core scenarios including AI Search and AI Bot. We build search question-answer cards via LLM-powered generation and develop official AI assistant, leveraging LLM ChatBot, Agent, and RAG technologies to deliver accurate, real-time responses. By closely integrating technical innovation with business scenarios, we bridge users with information and services, driving our business growth. Responsibilities: - Build AI Search Agents with planning, memory, and tool-use capabilities. Apply reinforcement learning, knowledge representation, and automated decision-making to solve complex problems end-to-end. - Enhance LLM-based query understanding: process multi-turn conversations, conduct semantic analysis on queries and documents, and ensure accurate, objective search results via LLM reasoning. - Optimize RAG and LLM pipelines: improve retrieval and ranking algorithms, train and optimize LLMs for answer generation (e.g., fine-tuning, inference optimization), and enhance the relevance and structured presentation of search results. - Translate research (e.g., Agent/LLM innovations) into production, collaborating with cross-functional teams to drive product upgrades.

任职要求

Minimum Qualifications - 5 years of experience in Machine Learning, preferably in a fast-paced, high-growth environment. - Proficient in C++/Python; strong command of algorithms and data structures (e.g., retrieval, graph algorithms). - Basic understanding of LLM fundamentals (e.g., prompt engineering, fine-tuning workflows). - Strong problem-solving skills with a track record of delivering technical projects. Preferred Qualifications - Hands-on experience with LLM/Agent technologies: RAG optimization, RLHF/SFT for LLMs, or Agent framework development (e.g., planning/memory modules). - Excellent communication and collaboration skills, with a track record of exploring new technologies and driving technical advancements with teams.

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