Assess the security of AI-powered features and internal AI tooling — prompt injection, unsafe tool/agent invocation, data leakage and over-permissive integrations.
Analyze e-commerce data-related business problems and abstract them into data-related requirements that are then implemented to help resolve key business problems.
Assess the security of AI-powered features and internal AI tooling — prompt injection, unsafe tool/agent invocation, data leakage and over-permissive integrations.
This internship provides exposure to the full development pipeline, including data collection, model training, robot integration, simulation, real-world testing, and failure analysis.
We are looking for a hands-on Embodied AI Engineer with an interest in robot learning and humanoid robotics. You will support the development, fine-tuning, deployment, and validation of intelligent behaviors on physical humanoid robots.
AI scenarios: Participate in the application of AI in client-side development and core business scenarios, helping build an AI-driven R&D and system capability framework. This includes, but is not limited to, improving development efficiency through AI, such as code generation, workflow-driven development, and business understanding; participating in the design and practice of AI Native / Agentic Commerce capabilities, including interaction models and capability abstractions for agents; exploring and contributing to cutting-edge AI payment capabilities and industry-standard protocols; and promoting intelligent capabilities across payment and financial flows, such as decision intelligence, recommendation intelligence, and experience intelligence, to continuously improve engineering efficiency and business outcomes.
Build scalable and reliable Extract, Transform and Load (ETL) processes for the ingestion and integration of large, structured and unstructured data from a variety of data sources, including systems that are on-premise and on cloud platforms.
Design and build scalable data flows, data warehouses, scheduling systems, query engines, data services, analytics systems, process standards, and data tools/products to lower the barrier to data usage, ensure stable and efficient system operations, and maximize data value.
Explore the application and implementation of cutting-edge research such as large language models, graph neural networks, and multimodal techniques in financial risk scenarios.