LLM Algorithm Engineer (Post Training)

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.

• 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