Develop and improve LLM-powered chatbot and AI agent solutions for seller-facing scenarios.
Explore agent design, harness engineering, and skill design to improve the reliability, effectiveness, and scalability of AI applications.
Mine and develop reusable AI skills from seller queries, conversation logs, business workflows, and existing knowledge.
Analyze chatbot interaction logs and bad cases, identify failure patterns, and improve prompts, skills, tools, workflows, and agent decision-making.
Explore self-improvement agent approaches that can automatically evaluate, diagnose, and iteratively optimize chatbot capabilities.
Design evaluation frameworks and experiments to measure chatbot quality, task completion, accuracy, and business impact.
Work closely with product managers, business teams, and engineers to understand seller needs and translate them into practical AI solutions.
Deep dive into development lines, learning and understanding the mechanism of every application component, and promoting product scalability, stability, and performance;
Setup, manage, and maintain Shopee product/middleware/big-data applications and services.
Perform regular and ad-hoc server-side deployments, performance fine-tuning, and troubleshooting.
Design and develop an automated technical operation platform.
Capacity and Resource management.
Responsible for the full-chain stress test to enhance the performance and remove redundancy of applications.
Prepare routine operation documentation.
Design and build high-performance backend services and distributed systems that power personalized vouchers, promotions, and campaign experiences across Shopee’s key product surfaces.
Partner closely with Product, Business, Data Science, Ads, Search, and Recommendation teams to translate marketplace growth opportunities into reliable, scalable, and production-ready engineering solutions.
Productionize machine learning and optimization capabilities by integrating model serving, experimentation, and decisioning logic into low-latency online systems.
Apply advanced AI technologies, including LLMs, and AI agents, to improve the efficiency, quality, and automation of e-commerce campaign operations.
Develop core capabilities for personalized recommendations, promotion and voucher optimization, campaign budget planning, and product demand forecasting.
Ensure production reliability through resilient API design, caching, asynchronous processing, graceful degradation, observability, and continuous performance optimization.
Work closely with the promotion product team, business partners, and internal stakeholders to identify marketplace growth challenges, uncover actionable insights, and deliver practical, scalable solutions based on large-scale user and product data.
Collaborate with product and engineering teams to design and build end-to-end machine learning pipelines to power data-driven decision-making for promotions and campaign planning.
Apply cutting-edge AI technologies such as LLMs, Agents, and RL to improve efficiency of building e-commerce campaigns.
Work on core solutions related to personalized recommendations, promotion and voucher configuration optimization, campaign budget planning, and product demand forecasting.
Develop, iterate, and maintain machine learning models, including those based on deep learning, reinforcement learning, and multi-armed bandit frameworks, applied in areas such as recommendation, search, and ads ranking.
Conduct rigorous online and offline evaluations, including A/B testing, to ensure model effectiveness and business impact.
Design and develop Shopee Infrastructure Platforms to serve millions of users across the world, ensuring thousands of production systems run smoothly and reliably in our platforms, no matter big campaigns or normal days.
Evolve Shopee infrastructures and empower businesses via Cloud Native technology stacks and Machine Learning technologies.
Work closely with Site Reliability Engineers, Machine Reliability Engineers and other engineers to continuously improve the development, delivery efficiency and quality of applications via Shopee Infrastructure Platforms.
Keep improving the Shopee Infrastructure Platforms' availability, stability, and security via systematic ways, such as TDD, Chaos Engineering, Reliability Engineering, Disaster Resilience, etc.
Design and develop code based on business requirements
Support data calculation needs for intelligent risk decisions. Delivering high-quality and efficient data computing platforms and products
Participate in offline and real-time data development to support risk analysis, model training, and other risk-related data needs
Write clean, efficient, and maintainable code
Participate in the design, development, and continuous optimization of digital banking backend services and technical platforms, delivering high-quality, maintainable code.
Contribute to server-side architecture design to build high-performance, highly available, secure, and scalable distributed systems.
Deeply understand business requirements, collaborating with Product Managers and cross-functional teams to translate business needs into technical designs and scalable features.
Continuously optimize system performance, stability, resource utilization, and user experience for high-concurrency banking scenarios.
Design and develop internal developer tools, automation scripts, and workflows to boost R&D efficiency and engineering quality.
Explore emerging AI technologies (e.g., LLMs, AI Agents, AIGC) and evaluate their applications in banking business scenarios and intelligent R&D practices.
Leverage AI-assisted coding methodologies (e.g., Vibe Coding / AI copilots) to streamline code generation, testing, and delivery processes.
Monitor and analyze security related alerts through the various platforms. Conduct timely investigation of alerts, perform analysis and event triage.
Leverage security research, security assessment, operations, and incident response to ensure security across different products and systems.
Stay updated with industry trends and continuously improve the security of the company.
In a data-driven and evaluation-driven manner, build an efficient closed loop for data iteration and establish an end-to-end data system spanning data sourcing, labeling, processing, synthesis, and evaluation. Continuously build high-quality datasets and evaluation sets to keep improving foundation model capabilities and to drive the development of AI models and applications.
Responsibilities cover one or more of the following directions:
Design and implement high-performance, scalable, and distributed data infrastructure covering the full lifecycle — data storage, ingestion, cleaning, labeling, management, and analysis — and continuously improve data engineering efficiency.
Design audio-visual multimodal training data strategies; develop efficient data processing, synthesis, and optimization operators and pipelines, and build a multimodal data asset repository to meet the data needs of large model development.
Build a "data–model–evaluation" closed loop together with Agents, using data to drive rapid iteration of large models.
Track cutting-edge techniques and methods in the large-model data domain, explore innovative approaches such as data augmentation, dat
Algorithm Development: Design and develop innovative recommendation algorithms tailored to various e-commerce scenarios to enhance user experience and conversion rates.
Model Optimization: Continuously iterate and optimize existing models to improve efficiency, accuracy, and scalability.
Data Analysis: Conduct in-depth analysis of user behavior data to uncover user preferences and improve model effectiveness.
Business Insight: Understand business goals and design algorithmic solutions that align with and support these objectives.
Innovation & Trends: Stay up-to-date with the latest developments in recommender systems and related technologies, and explore opportunities to integrate state-of-the-art (SOTA) methods.
Take part in product requirement analysis and feasibility involving various features to support different promotion domains.
Designing technical solutions for the requirements with detailed analysis and documentation.
Building large scale distributed systems optimized for performance, availability and scalability with critical monitoring.
Writing clean, testable, extendable and maintainable code using engineering best practices.
Be a team player by communicating & collaborating with other team members effectively to deliver the projects.
Design and develop high-concurrency, highly available, and high-performance payment and financial systems.
Build critical payment modules with strict requirements for fund security, data integrity, consistency, and disaster recovery.
Participate in end-to-end product delivery, from business understanding and system design to implementation, testing, deployment, and operations.
Use AI coding tools throughout the development lifecycle, including design, coding, testing, debugging, refactoring, and code review.
Build AI-native engineering practices, including Agent Harnesses, engineering Knowledge Bases, AI Loops, workflow automation, and knowledge reuse.
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.
Design and develop core agent algorithms, including multi-turn dialogue planning, tool orchestration (retrieval, ranking, LLM synthesis), and adaptive task planning.
Build and maintain agent memory architectures using knowledge graphs to support long-term consistency, personalization, and context retention across sessions.
Develop emotion-aware dialogue modeling techniques to improve agent naturalness, consistency, and user engagement.
Design and implement LLM alignment and safety strategies (e.g., SFT, DPO) to mitigate risks such as implicit persuasion or psychological manipulation in personalized generation.
Build multimodal agent capabilities that integrate vision-language reasoning with dialogue planning for tasks such as tutoring or adaptive guidance.
Benchmark and deploy LLMs/VLMs across GPU clusters and cloud environments (e.g., AWS); build reproducible evaluation pipelines to support model and architecture selection.
Track frontier research in agent algorithms, contribute to publications, and represent findings at top-tier AI/NLP venues.
Build core Agent logic, including but not limited to task planning and orchestration, tool calling, multi-turn dialogue management, memory, RAG, context engineering, and multi-agent collaboration.
Lead Continuous Pre-training and Post-training for vertical domains and business scenarios, including building high-quality datasets and data pipelines, designing RL reward models, improving instruction following and reasoning capabilities, task completion, role-playing, anthropomorphic and personalized dialogue, proactive/reactive immersive multimodal conversation experiences, and enhancing the model's IQ and EQ.
Build long-term and short-term memory architectures, addressing issues such as forgetting and attention dispersion in long contexts, and improving immersion and consistency in long-term user interactions.
Build multimodal RAG systems, including development and optimization of key modules such as recall, ranking, long-text processing, and multi-document synthesis.
Develop the Agent's tool layer, integrating external APIs and MCP such as search, code interpreters, browsers, sandboxes, and third-party services.
Design and tune prompts and context management, with tailored optimization for different product requirements.
Design scientifically rigorous quantitative evaluation systems and plans aligned with product requirements; continuously monitor product metrics and provide guidance for Agent and model optimization.
Explore innovative AI applications.
Design, build, and ship scalable cloud-native backend services from concept to production.
Translate stakeholder needs into robust technical solutions and AI-powered product workflows, spanning estimation, design, implementation, testing, deployment, and monitoring to improve user experience, productivity, and ways of working across the organization.
Collaborate closely with backend engineers and cross-functional teams throughout analysis, development, and launch.
Leverage AI-native engineering practices, including AI-assisted development, agentic programming, and harness engineering, to accelerate delivery while writing clean, maintainable, and well-tested code with strong ownership of quality through reviews and documentation.
Plan and support infrastructure, deployment, and operational readiness for new features and services.
Continuously improve system performance, reliability, scalability, and developer efficiency.
Design, build, and ship scalable cloud-native backend services from concept to production.
Translate stakeholder needs into robust technical solutions and AI-powered product workflows, spanning estimation, design, implementation, testing, deployment, and monitoring to improve user experience, productivity, and ways of working across the organization.
Collaborate closely with backend engineers and cross-functional teams throughout analysis, development, and launch.
Leverage AI-native engineering practices, including AI-assisted development, agentic programming, and harness engineering, to accelerate delivery while writing clean, maintainable, and well-tested code with strong ownership of quality through reviews and documentation.
Plan and support infrastructure, deployment, and operational readiness for new features and services.
Mentor and coach junior engineers through code reviews, technical guidance, knowledge sharing, and hands-on support to help improve team capability and engineering standards.
Continuously improve system performance, reliability, scalability, and developer efficiency.
Design, build, and iterate on AI Agent capabilities (e.g. agent orchestration, tool/function calling, retrieval-augmented generation, multi-step workflow automation) for a core enterprise product.
Work closely with the Product Manager to translate product requirements into technical designs, quick prototypes, and production-ready features.
Develop and integrate LLM-powered features (copilot, smart search, summarization, agentic workflows) into our core enterprise product, transforming how users search, collaborate, and get work done.
Build evaluation pipelines and instrumentation to continuously measure and improve agent accuracy, latency, and reliability in production.
Rapidly prototype new ideas, run experiments, and iterate based on user feedback and product metrics.
Partner with backend/infra teams to ensure AI features are scalable, secure, and ready for enterprise use.
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