Algorithm Engineer, Paid Ads (Campus Recruitment 2026)
Job Description
Job Description:
- Enhance existing recall, pre-ranking, ranking, re-ranking modules in our large scale ads platform, and continuously improve ads quality.
- Iterate ads core models, including but not limited to CTR/CVR, expand and optimize feature set, search and create new model architecture.
- Optimize ads bidding, deduction and traffic allocation strategies to create better ads bidding mechanism and increase ads monetisation efficiency.
- Focus on the research of search, recommendation and ads to apply relevant strategies and adapt to our platform for implementation in our production system.
Requirements:
- Bachelor's Degree or above in Computer Science or a related field
- Deep understanding in machine learning and deep learning theory, publications in machine learning and data mining or related conferences is a bonus.
- Sensitive to data and business, passionate about technical challenges, have excellent critical thinking skills.
- Be familiar with big data processing tools, have experience with one or more of the following tools: Hadoop, Spark, Hive and Flink etc.
- Effective team player and has good communication skills.
- Proactive and self-motivated.
- Preferred to have Ads algorithm internship experience.
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About Company
Shopee Pte. Ltd. is a Singaporean multinational technology company that specialises in e-commerce. The company was launched in Singapore in 2015, before it expanded abroad. As of 2021, Shopee is considered the largest e-commerce platform in Southeast Asia with 343 million monthly visitors.
Skills:
XGBoost, Cpt, multilingual unstructured data sources, large language models, token-based generative pre-training, autonomous agents, domain-specific data synthesis, CTR prediction, SFT, multi-stage data curation, content generation, MoE architectures, Bayesian modeling, LightGBM, multimodal LLM applications, semantic search, RL-based post-training methods, LangGraph, Plan-and-Execute agents, DAPO, data-mixture optimization techniques, generative representation-learning pipelines
Skills:
Retrieval-Augmented Generation (RAG), Large Language Models (LLMs), SGLang inference architectures, vLLM, Prompt engineering, SFT, DPO, English and Chinese communication, Agent orchestration, Memory sharing, Quantization, Evaluation pipelines, Inference optimization, Human feedback, Task decomposition, Automatic metrics