Machine Learning Engineer Graduate (Global E-Commerce, Agentic Search) - 2027 Start

About the Team Our team is responsible for the search algorithm for our rapidly growing global e-Commerce business. We use state-of-the-art large-scale machine learning technology, the cutting-edge NLP, CV and multi-modal technology to build the industry's top-class search engine to provide the best e-Commerce search experience, for more than 1 billion monthly users around the world. Our mission is to build a world where "there is no hard-to-sell good-priced product in the world". We are looking for talented individuals to join our team. As a graduate, you will get opportunities to pursue bold ideas, tackle complex challenges, and unlock limitless growth. Successful candidates must be able to commit to an onboarding date by the end of the year. Please state your availability and graduation date clearly in your resume. Candidates can apply to a maximum of two positions and will be considered for jobs in the order you apply. The application limit is applicable to our Company and its affiliates' jobs globally. Applications will be reviewed on a rolling basis - we encourage you to apply early. Responsibilities - Develop algorithms for our next-generation Agentic Search engine, covering the full search pipeline: query understanding, planning, retrieval, ranking, RAG and generation. The goal is to improve relevance, answer quality and user experience. - Build Agentic Search capabilities such as Search Agent, Deep Search and Deep Research, applying Planning, Tool Use, Multi-hop Retrieval, Grounding, Citation and Self-Verification in real search scenarios. - Train the Search Foundation Model and build the data–evaluation–training loop. This includes SFT, Preference Learning, RLHF/RLVR, Distillation, Synthetic Data and Long Context, as well as LLM-as-a-Judge and rubric-based automated evaluation built from search logs. - Work with product and engineering teams to ship intelligent search to hundreds of millions of users. - Follow frontier research in LLMs, Agents, multimodality, RL, Memory and Inference-time Compute. Qualifications Minimum Qualification(s) - Individuals who are completing or have recently completed a Bachelor's degree in Software Development, Computer Science, Computer Engineering, or a related technical discipline. - Strong coding skills in C/C++ or Python, and a solid grounding in data structures and algorithms. - Familiar with ML/DL fundamentals (Transformer, Attention, Embedding) and PyTorch. - Research or project experience in search, recommendation, ads, IR, NLP, LLMs, multimodality or RL. - Strong problem-solving, learning and collaboration skills. Preferred Qualification(s) - In-depth research or project experience in Agentic Search, Deep Research, RAG, LLM Post-Training or RLHF/RLVR. - Publications at top venues such as KDD, SIGIR, WWW, NeurIPS, ICML, ICLR, ACL, EMNLP or CVPR. - Awards in competitions such as ACM/ICPC, NOI/IOI or Kaggle. - Experience with large-scale search or recommender systems, distributed training, or LLM inference optimization.