Agentic Commerce AI Agent Algorithm Engineer

Design and optimize a consumer-facing e-commerce Shopping Agent, covering intent understanding, task planning, query rewriting, product search/recommendation, tool calling, memory, and multi-turn dialogue. Develop and implement post-training pipelines such as SFT, DPO, PPO/RLHF, and Reward Modeling, covering data construction, training, evaluation, deployment, and regression.

Improve tool-use accuracy, instruction following, product relevance, factual consistency, personalization, and hallucination control through data and model alignment. Build post-training datasets and evaluation systems combining human evaluation, automated metrics, and LLM-as-a-Judge. Drive continuous optimization based on offline evaluation, A/B testing, user experience, conversion, latency, cost, and stability.

Collaborate with product, engineering, search/recommendation, and operations teams to deploy Agent capabilities in large-scale e-commerce scenarios. Master’s degree or above in Computer Science, AI, or a related field At least three years of experience in machine learning, NLP, search/recommendation, or LLM applications.

Hands-on experience with at least one post-training method, such as SFT, DPO, PPO/RLHF, or Reward Modeling. Familiarity with training frameworks such as Megatron-LM, veRL, or Deep. Speed, and basic knowledge of distributed training. Experience with Agent, Function Calling, Tool-use, multi-turn dialogue, and LLM evaluation.

Strong Python, engineering, data-analysis, and production problem-solving skills. Experience with e-commerce AI, large-scale post-training, Agent RL, self-play, or synthetic data is a strong plus.