Agentic Commerce AI Agent Algorithm Engineer

Department Engineering and Technology

LevelExperienced (Individual Contributor)

LocationSingapore

The Engineering and Technology team is at the core of the Shopee platform development. The team is made up of a group of passionate engineers from all over the world, striving to build the best systems with the most suitable technologies. Our engineers do not merely solve problems at hand; We build foundations for a long-lasting future. We don't limit ourselves on what we can or can't do; we take matters into our own hands even if it means drilling down to the bottom layer of the computing platform. Shopee's hyper-growing business scale has transformed most "innocent" problems into huge technical challenges, and there is no better place to experience it first-hand if you love technologies as much as we do.

About the Team:

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.

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.