Develop accurate sales forecasts, manage pipeline health, and lead business reviews to support predictable revenue growth.
Partner closely with Solutions Architects, Product, Marketing, and Customer Success teams to deliver customer-centric solutions and maximize business outcomes.
Gather market intelligence and customer insights to help shape product strategy and commercial priorities.
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AI scenarios: Participate in the application of AI in front-end 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.
Participate in the development of core Global Payment web capabilities, including payment & wallet products, financial product scenarios, and front-end architecture.
Design and build highly reliable web checkout and transaction front-ends for TikTok Pay wallet and key financial products (BNPL, insurance, consumer credit).
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Optimize system performance across latency, throughput, and cost dimensions by improving prompt chains and implementing scalable caching strategies for AI features serving large user bases.
Develop evaluation frameworks and guardrails, including LLM-as-a-judge and human-in-the-loop approaches, to ensure outputs are safe, accurate, and reliable, especially for compliance-sensitive industries such as finance. Evaluate multiple models on common tasks to identify trade-offs and inform go-to-market decisions.
Collaborate closely with product and algorithm research teams to feed real-world customer signals back into the product roadmap and model development process.
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Drive PoC-to-production conversion and ensure measurable business impact.
Build technical credibility within customer organizations, from engineering teams to C-level executives.
Participate in or lead the design and implementation of Generative AI applications, including LLMs, RAG systems, agent architectures, and multimodal solutions.
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Optimize system performance across latency, throughput, and cost dimensions by improving prompt chains and implementing scalable caching strategies for AI features serving large user bases.
Develop evaluation frameworks and guardrails, including LLM-as-a-judge and human-in-the-loop approaches, to ensure outputs are safe, accurate, and reliable, especially for compliance-sensitive industries such as finance. Evaluate multiple models on common tasks to identify trade-offs and inform go-to-market decisions.
Collaborate closely with product and algorithm research teams to feed real-world customer signals back into the product roadmap and model development process.
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Follow up on cutting-edge technologies such as global large model inference, GPU high-performance computing, distributed parallelism, and cache optimization, benchmark against mainstream inference frameworks such as vLLM and TensorRT-LLM, complete the implementation of solutions and technological innovation, continuously iterate and optimize the performance and cost advantages of the inference system, and build the core technological barriers of the team.
Individuals who are completing or have recently completed a Bachelor's/ Master's degree in computing or a related discipline.
Solid foundation in computer low-level knowledge, proficient in C/C++ and Python programming, skilled in CUDA programming and familiar with GPU hardware architecture principles, and well-versed in GPU memory models, computing scheduling, and communication mechanisms;
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Collaborate with the business and algorithm teams to identify performance issues, provide full stack performance analysis, bottleneck diagnosis, and optimization solutions, consolidate general-purpose performance optimization components, toolchains, and platform capabilities, and empower multiple internal business.
Individuals who are completing or have recently completed a Bachelor's/ Master's degree in computing or a related discipline.
Familiar with mainstream model compilation stacks (such as TVM, MLIR, XLA, etc.), with relevant experience in development, and optimization;
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Individuals who are completing or have recently completed a Bachelor's/ Master's degree in computing or a related discipline.
Familiar with basic Linux commands, with solid C/C++ programming skills and knowledge of data structures and algorithms
Familiar with the basic principles of multi-threaded concurrency, proficient in basic usages such as thread usage, synchronization locks, and thread pools, able to identify common concurrency issues, and possess the ability to perform basic performance tuning in multi-threaded scenarios
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