Agentic Commerce AI Agent Algorithm Engineer
Job Description
Job Description:
- 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.
Requirements:
- 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 DeepSpeed, 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.
More Info
Key Skills
RLHF
Agent Function Calling
data-analysis
Tool-use
multi-turn dialogue
DPO
LLM evaluation
SFT
Reward Modeling
search recommendation
veRL
DeepSpeed
LLM applications
Megatron-LM
Python engineering
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