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 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.
Own the architecture, development, and productionization of core AI data product capabilities, including natural language to SQL, dashboard generation, and insight / report generation.
Build production-grade LLM application pipelines for enterprise data scenarios, including:
metadata and schema retrieval
semantic layer and metric understanding
query planning and SQL generation
SQL validation, rewriting, and execution
visualization spec generation
narrative insight generation
Design and implement reliable backend services and platform capabilities that balance accuracy, access control, explainability, observability, latency, and cost.
Deeply understand user workflows in analytics and data consumption, and translate ambiguous business questions into scalable, reusable engineering systems.
Work closely with Data Infra, Data Engineering, BI, Frontend, Design, and AI / ML engineering teams to deliver products from zero to one and scale them from one to many.
Establish and continuously improve the LLM evaluation and feedback loop, including offline benchmarks, online metrics, user feedback, prompt / model versioning, failure analysis, and quality improvement.
Drive performance, reliability, and engineering excellence across the system, and contribute to long-term architectural evolution.
Stay current with best practices in LLM applications, AI agents, semantic analytics, and enterprise AI systems, and turn them into practical production solutions.
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.
We are looking for a (Senior/Expert) System Devops Engineer who will be primarily responsible for the frontend development of our internal systems and tools. You will work closely with the product manager and backend developers to create intuitive, high-performance, and reliable web applications. While frontend work will be the main focus, the ability to contribute to backend development is a strong plus.
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.
