携程Performance Marketing Growth Manager(MJ035997)
任职要求
5–10年渠道增长、投放运营或创意运营相关经验，具备跨部门项目推进经验；旅游/本地生活、电商、内容平台背景优先。熟悉主流渠道（如Google，Facebook等）内容与投放逻辑，理解素材对转化与下单的影响路径。数据分析能力强，能基于数据定位问题、设计测试与落地优化；熟悉A/B测试方法与常见BI工具。优秀的项目管理与沟通协同能力，有多地区/多语言项目协同经验者优先。
Job Description
1. Cross-Functional Workflow & Mechanism DevelopmentLead cross-departmental collaboration mechanisms for medium-to-large-scale campaigns, consolidating regional BUs, middle-office teams, channel operations, and creative units. Establish a closed-loop process covering: requirement collection → creative production → asset deployment → media buying → data feedback → post-campaign review and iteration.Define milestones, deliverables, and cadence (weekly / bi-weekly / project milestones). Select priority projects to build joint execution plans that support channel KPI achievement under the principle of "zero incremental burden to channels."Data alignment: standardize metric definitions and dashboards (e.g., CPI, ROAS), conduct regular alignment sessions and project retrospectives, and generate issue trackers with closed-loop improvement actions.2. Evergreen Campaign Asset ManagementMap end-to-end workflows with channel partners; establish evergreen asset refresh cycles (upload cadence, version control, sunset criteria, archiving, and reuse). Develop channel ×…工作职责
1. 协同链路与机制建立牵头中大型Campaign的跨部门协作机制，聚合regional BU、中台、渠道投放、创意组，建立需求收集—创意产出—素材上架—投放—数据回传—复盘迭代的闭环。明确里程碑、交付物与节奏（周/双周/项目里程碑）。选取重点项目建立联合计划，在“不增加渠道负担”的原则下支持渠道KPI达成。数据与对齐，统一指标口径与看板（如CPI, ROAS等），开展定期对齐与项目复盘，形成问题清单与改进闭环。2. Evergreen Campaign 素材管理梳理与渠道方配合的全流程链路，建立Evergreen素材更新迭代周期表（上新节奏、版本控制、淘汰标准、归档与复用），按渠道×国家细化素材包与有效风格指南，明确合规与品牌规范。组织常态化A/B测试与快速试错，定期淘汰低效素材，沉淀最佳实践与复用模板。3. 与产品，研发等部门协作，利用AI提升素材质量与效率从高效广告中批量化提取创意因子，建立特征库并在不同主题与品类上迭代验证。接入自动化能力，提升素材铺设的效率。
We are now looking for a Performance Engineer Intern to support our growing investments in perf testing of various company datacenter products and applications. Today, NVIDIA is tapping into the unlimited potential of AI to define the next era of computing. An era in which our GPU acts as the brains of computers, robots, and self-driving cars that can understand the world, all while striving to deliver the highest possible performance of our products.You will be part of global Performance Lab team, improving our capacity to expertly and accurately benchmark state-of-the-art datacenter applications and products. We also work to develop new scripts that enhance the team’s ability to gather data through automation and designing efficient processes for testing a wide variety of applications and hardware. The data that we collect drives marketing/sales collaterals as well as engineering studies for current and future products. You will have the opportunity to work with multi-functional teams and in a dynamic environment where multiple projects will be active at once and priorities may shift frequently. What you’ll be doing: • Benchmark, profile, and analyze the performance of AI workloads specifically tailored for large-scale LLM training and inference, as well as High-Performance Computing (HPC) on NVIDIA supercomputers and distributed systems. • Aggregate and produce written and visual reports with the testing data for internal sales, marketing, SW, and HW teams • Setup and configure systems with appropriate hardware and software to run benchmarks • Collaborate with internal teams to debug and improve performance issues • Develop Python scripts to automate the testing of various applications • Assist with the development of tools and processes that improve our ability to perform automated testing
We are now looking for a GeForce/ProViz Performance Engineer Intern! This position offers the chance to create a significant impact in a dynamic, technology focused company. As a member of the Performance Lab team, you will reach firsthand GPUs and optimize performance from designing stage till whole product lifetime, architectures to extend the state of the art in Gaming, Professional Visualization, Cloud Gaming, Data Center efficiency and performance. What you’ll be doing: • Identify, run graphics, studio and WinAI benchmarks across servers, PCs, workstations and laptops. • Compose competitive analysis reports for internal and external customers to position NVIDIA products appropriately using their evaluation. • Develop and maintain automation scripts for games/studio/WinAI performance and system monitoring data collection on Windows and Linux to speed up providing business and engineering insights. • Develop, implement and maintain tools to improve testing efficiency.
We are now looking for a Performance Engineer Intern to support our growing investments in perf testing of various company datacenter products and applications. Today, NVIDIA is tapping into the unlimited potential of AI to define the next era of computing. An era in which our GPU acts as the brains of computers, robots, and self-driving cars that can understand the world, all while striving to deliver the highest possible performance of our products.You will be part of global Performance Lab team, improving our capacity to expertly and accurately benchmark state-of-the-art datacenter applications and products. We also work to develop infrastructures and solutions that enhance the team’s ability to gather data through automation and designing efficient processes for testing a wide variety of applications and hardware. The data that we collect drives marketing/sales collaterals as well as engineering studies for future products. You will have the opportunity to work with multi-functional teams and in a dynamic environment where multiple projects will be active at once and priorities may shift frequently. What you’ll be doing: • Benchmark, profile, and analyze the performance of AI workloads specifically tailored for large-scale LLM training and inference, as well as High-Performance Computing (HPC) on NVIDIA supercomputers and distributed systems. • Aggregate and produce written reports with the testing data for internal sales, marketing, SW, and HW teams. • Develop Python scripts to automate the testing of various applications. • Collaborate with internal teams to debug and improve performance issues. • Assist with the development of tools and processes that improve our ability to perform automated testing. • Setup and configure systems with appropriate hardware and software to run benchmarks.
• Writing highly tuned compute kernels to perform core deep learning operations (e.g. matrix multiplies, convolutions, normalizations) • Following general software engineering best practices including support for regression testing and CI/CD flows • Collaborating with teams across NVIDIA:• CUDA compiler team on generating optimal assembly code • Deep learning training and inference performance teams on which layers require optimization • Hardware and architecture teams on the programming model for new deep learning hardware features