LLM Algorithm Engineer (Post Training)

About Us

Sea Group is establishing a brand-new, strategic AI department. This department is dedicated to exploring the transformative potential of generative AI in revolutionizing human connection, self-expression and communication diversity, and social interaction. We are building the next generation of AI-native applications and a comprehensive Model-as-a-Service (MaaS) product support system. Based on massive multi-country data, we are building a leading multilingual AI ecosystem from the ground up. We look forward to more outstanding talents joining us to build leading Southeast Asian multilingual models and explore innovative AI-native applications.

The AI application team focuses on the intersection of social connectivity and artificial intelligence. Our mission is to leverage LLMs to create digital personas that can act as personal assistants and social bridges. This team operates with a startup's agility backed by our Group's robust resources, aiming to define how humans interact in the AI era.

Responsibilities

Post-Training Pipeline Implementation

• Participate in the development and deployment of post-training pipelines such as SFT, DPO, PPO, and Reward Modeling; responsible for concrete execution from training, hyperparameter tuning, evaluation to online regression.
• Participate in training optimization in Agent and Tool-use directions, improving metrics such as tool-calling accuracy, multi-turn instruction following, and hallucination control.
• Participate in iteration of core-scenario models such as Router, Query rewriting, Agent tool calling, Web Search decision-making, Memory, and e-commerce search relevance.

Data and Evaluation

• Manage the collection, cleaning, synthesis, and annotation workflows for post-training data; participate in annotation guideline development and vendor coordination.
• Build and maintain automated evaluation combining human review and LLM-as-Judge; produce reproducible effectiveness reports.
• Keep up to date with frontier methods in the industry and experiment with them based on the team's direction.

Requirements

• Master's degree in Computer Science, AI, or a related field.
• Prior development experience in at least one LLM post-training area (SFT, DPO, PPO, RLHF, Reward Modeling, etc.); able to independently complete key steps from data through training to evaluation.
• Prior experience with at least one mainstream training framework (e.g., Megatron, veRL, etc.) and understanding of basic principles of distributed training.
• Understanding of Agent / Tool-use / multi-turn dialogue; familiar with data construction and basic alignment approaches for Function Calling.
• Strong engineering and troubleshooting capabilities; able to make reasonable trade-offs between effectiveness and iteration efficiency.
• Prior End-to-end post-training deployment experience, or participation in 1000-GPU-scale distributed training.
• Prior experience with leading large model teams will be a strong plus.
• A strong understanding or prior hands on experience in Agent RL, Self-play, synthetic data, or inference-time compute will be a strong plus