AI Engineer – LLM Algorithm Engineer (Agentic Commerce)

Shopee; Singapore; On-site; SGD 150,000 - 230,000; Full time

Job summary

Shopee is seeking an experienced AI/ML leader to drive continuous pre-training and post-training of large language models across vertical domains. You will design autonomous agents that extract and synthesize domain knowledge from multilingual sources, and develop RL-based methods to boost domain QA and reasoning performance.

You'll build multimodal LLM applications for content generation, semantic search recall, and business metrics like CTR, while integrating end-to-end agent workflows with

Qualifications

• Master's degree or above in Computer Science, NLP, AI, EEE, Signal Processing or related field.
• Minimum 5 years of full-time industry experience in LLM/ML algorithm engineering.
• Hands-on experience with large-scale model continual pre-training (Dense and MoE).
• Experience designing autonomous agent systems and LLM post-training techniques (SFT, RL-based methods).
• Experience fine-tuning/deploying multimodal large models with production impact.

Responsibilities

• Lead continuous pre-training and post-training of large language models for vertical business domains.
• Design and build autonomous agents that retrieve and synthesize domain knowledge from multilingual data sources.
• Develop RL-based post-training methods to improve QA, reasoning, and knowledge modeling.
• Build and optimize multimodal LLM applications for content generation, semantic search recall, and CTR prediction.
• Design end-to-end agent workflows integrating query understanding, content generation, validation, and downstream systems.
• Create generative representation-learning pipelines for downstream ranking and recommendation models.
• Collaborate cross-functionally to translate business requirements into scalable model training and agent system design.

Skills

LLM engineering

Autonomous agents

Reinforcement learning

Multimodal models

Data curation

Evaluation metrics

Programming

ML deployment

Education

Master's degree or above in CS/AI

Tools

LangGraph

Job description

• Lead continuous pre-training and post-training of large language models (Dense and MoE architectures) for vertical business domains, including domain-specific data synthesis, multi-stage data curation, and monthly iteration to support downstream business integration.
• Design and build autonomous agents (e.g., Plan-and-Execute agents) that automatically retrieve, extract, and synthesize domain knowledge from large-scale multilingual/unstructured data sources, producing high-confidence corpora for continued pre-training.
• Develop and iterate RL-based post-training methods (e.g., CPT, SFT, DAPO) to improve model performance on domain-specific QA, reasoning, and knowledge-modeling tasks.
• Build and optimize multimodal LLM applications for content generation and understanding, including product copywriting, semantic search recall, and business potential/CTR prediction.
• Design end-to-end agent workflows integrating query understanding, content generation, validation, and downstream product/search systems.
• Build generative representation-learning pipelines (e.g., token-based generative pre-training on behavioral sequences) to produce reusable embeddings for downstream ranking and recommendation models.
• Collaborate cross-functionally to translate business requirements into scalable model training and agent system design; continuously monitor production metrics to guide model and agent optimization.

Job Description

• Lead continuous pre-training and post-training of large language models (Dense and MoE architectures) for vertical business domains, including domain-specific data synthesis, multi-stage data curation, and monthly iteration to support downstream business integration.
• Design and build autonomous agents (e.g., Plan-and-Execute agents) that automatically retrieve, extract, and synthesize domain knowledge from large-scale multilingual/unstructured data sources, producing high-confidence corpora for continued pre-training.
• Develop and iterate RL-based post-training methods (e.g., CPT, SFT, DAPO) to improve model performance on domain-specific QA, reasoning, and knowledge-modeling tasks.
• Build and optimize multimodal LLM applications for content generation and understanding, including product copywriting, semantic search recall, and business potential/CTR prediction.
• Design end-to-end agent workflows integrating query understanding, content generation, validation, and downstream product/search systems.
• Build generative representation-learning pipelines (e.g., token-based generative pre-training on behavioral sequences) to produce reusable embeddings for downstream ranking and recommendation models.
• Collaborate cross-functionally to translate business requirements into scalable model training and agent system design; continuously monitor production metrics to guide model and agent optimization.

Requirements

• Master's degree or above in Computer Science, Natural Language Processing, Artificial Intelligence, Electrical and Electronics Engineering, Signal Processing or a related field.
• Minimum 5 years of full-time industry experience in LLM/ML algorithm engineering, including hands-on experience with large-scale model continuous pre-training (Dense and/or MoE architecture) for vertical business domains.
• Hands-on experience designing and building autonomous agent systems (e.g., using LangGraph or similar frameworks) for automated knowledge retrieval, extraction, and synthesis pipelines, combined with hands-on experience in LLM post-training techniques (SFT, RL-based methods such as DAPO) and data-mixture optimization techniques for pre-training data curation.
• Experience fine-tuning and deploying multimodal large models for content generation, semantic recall, or business potential prediction, with demonstrated production impact on key business metrics (e.g., CTR, conversion).
• Good programming and engineering skills; solid foundation in classic ML techniques (e.g., LightGBM, XGBoost, Bayesian modeling) is a plus.
• Good problem-solving skills; able to independently drive projects from research through to large-scale production deployment.
• Prior experience in multimodal retrieval/recommendation systems (e.g., cross-modal contrastive learning for content matching) will be a strong plus.