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.
Develop and maintain scalable and efficient Big Data platform components and OLAP engines.
Collaborate with cross-functional teams to integrate new features and technologies.
Ensure the reliability and performance of our data infrastructure.
Troubleshoot and resolve complex technical issues.
Continuously improve system architecture and design for optimal performance.
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.
Bachelor's degree or higher in Computer Science or a related field
Proficiency in at least one programming language, such as Python, C++ & Go
Skilled in at least one mainstream deep learning framework, like PyTorch, TensorFlow
Passion for LLM-related technology and solving challenging real-world problems
Deep understanding of computer fundamentals, including data structures, algorithms, and machine learning
Familiarity with mainstream algorithmic models in large language models, natural language processing, recommendation, and multimodality (including principles and implementation)
Prior research or internship experience in natural language Q&A or recommendation technology is preferred
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
Design, develop and maintain backend services
Collaborate with the product team and provide technical inputs to support product decision making
Participate in technical design discussions and contribute to the end solution
Execute and implement technical designs
Troubleshoot issues related to business logic or performance
Collaborating in a highly cross-functional environment with fellow Software Engineers, Product Managers, Quality Assurance Engineers, and Operation Engineers to deliver impact.
Adopting and integrating LLM/AI to improve the overall development workflow
Participate in the design and development of security platforms and surrounding infrastructure such as risk decision rule engine and feature store.
Design and write high-quality system and business code to meet user demands for platform capabilities.
Design and implement tools to help improve system stability and efficiency.
Stay updated with industry trends and continuously improve the platform's capabilities.
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.
You will join our Marketplace Intelligence & Data team to work on real-world identity verification and AI security problems, including:
Develop and improve computer vision and multimodal models, such as:
AIGC detection and media forensics
Face intelligence, liveness detection, and deepfake defense
Document understanding and fraud detection
Multimodal reasoning and AI agent
Participate in the full model lifecycle, including data analysis, model training, evaluation, and deployment
Design and implement training and inference pipelines using modern deep learning frameworks
Collaborate closely with senior engineers and product managers to deliver production-ready AI solutions
Continuously learn and explore new techniques in computer vision, multimodal AI, and trustworthy AI
Lead the architecture design and core capability development of the Business Security Platform, leveraging AI / LLM technologies to build end-to-end workflows for automated risk discovery, intelligent analysis, solution recommendation, and closed-loop risk governance.
Design and implement high-performance, highly available, and scalable platform services to support key scenarios such as anti-fraud, anti-scam, payment, and account security.
Build data computing and data service capabilities for risk decisioning, delivering high-quality, efficient, and reusable data computing products.
Research, evaluate, and apply industry best practices in AI Agents, LLM application engineering, and anti-fraud system engineering to drive technology adoption in business security scenarios.
Collaborate closely with product, algorithm, strategy, data, and platform teams to ensure smooth integration, continuous evolution, and scalable adoption of anti-fraud systems.
As a senior technical expert, lead critical technical reviews, solve complex engineering challenges, and improve engineering quality, technical standards, and talent development within the team.
Develop distributed Parameter Server (PS) systems for large-scale sparse model training and inference platforms in the search, advertising, and recommendation domains. The system should support high-throughput parameter read/write and update operations, handle hundreds of billions of features and TB-level sparse models, enable online real-time learning, and meet algorithmic needs such as feature admission and expiration.
Participate in the development of the one-stop machine learning platform, integrating the PS system into the platform to provide a user-friendly, stable, high-performance, and platform-level distributed parameter service system. Enhance the platform’s efficiency and usability, accelerating the model iteration process for algorithm teams.
Develop distributed Parameter Server (PS) systems for large-scale sparse model training and inference platforms in the search, advertising, and recommendation domains. The system should support high-throughput parameter read/write and update operations, handle hundreds of billions of features and TB-level sparse models, enable online real-time learning, and meet algorithmic needs such as feature admission and expiration.
Participate in the development of the one-stop machine learning platform, integrating the PS system into the platform to provide a user-friendly, stable, high-performance, and platform-level distributed parameter service system. Enhance the platform’s efficiency and usability, accelerating the model iteration process for algorithm teams.
Responsible for the architecture design and core capability building of the business security platform, leveraging AI/LLM technologies to create end-to-end capabilities such as automatic risk discovery, intelligent analysis, solution recommendation, and closed-loop governance.
Design and implement high-performance, highly available, and scalable platform services to support key business scenarios such as anti-fraud, payment, and account security.
Build data computing and data service capabilities for risk control decision-making, delivering high-quality, efficient, and reusable data computing products.
Track and evaluate industry best practices in areas such as AI Agent, LLM application engineering, and anti-fraud system engineering, promoting the implementation of new technologies in business security scenarios.
Collaborate closely with product, algorithm, strategy, data, and platform teams to promote the smooth integration, continuous evolution, and large-scale application of the anti-fraud system.
As a senior technical expert, responsible for reviewing key technical solutions, tackling complex problems, and promoting the improvement of team engineering quality, technical specifications, and talent development.
Design and implement high-performance, highly stable platform services
Support data calculation needs for intelligent risk decision. Delivering high-quality and efficient data computing products
Participate in offline and real-time data development to support risk analysis, model training, and other risk-related data needs
Research, evaluate, and apply industry best practices and new technologies in anti-fraud system engineering
Collaborate closely with product, algorithm/strategy, and platform teams to ensure smooth integration and evolution of the anti-fraud system.
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.
Responsible for the development and maintenance of Shopee's e-commerce search user-end products, efficiently supporting business operations and continuously improving the user search experience.
Participate in the evolution of the search backend systems and architecture, continuously enhancing performance, stability, and resource efficiency to safeguard business-side innovations and improve personal technical skills.
Stay updated on the latest trends in search business and technology, responsible for the introduction and implementation of new technologies, providing more possibilities for business innovation.
Design and develop backend services for large-scale data platforms, including data development, scheduling, data assets, and analytics services
Build and evolve core system modules and frameworks with a focus on scalability, stability, and maintainability
Abstract complex data models and computation logic into clear, user-facing platform capabilities
Troubleshoot and solve complex technical and business problems in distributed systems
Contribute to system reliability, performance optimization, and high-availability design
Collaborate closely with product managers, data engineers, and frontend engineers
