安克创新SOLIX - 大充销售leader

1. 客户开发与管理 - 开拓海外KA客户资源，包括新能源行业头部渠道商、能源电力公司、系统集成商（SI）、零售/电商平台、ODM/OEM合作方等； - 维护客户长期关系，主导合同谈判及订单落地，协调资源解决客户需求（技术支持、认证支持、售后服务等）； - 分析客户业务痛点，定制化解决方案（如光储充系统、户储+微电网集成方案等）。 2. 销售策略与执行 - 制定区域市场拓展计划，管理销售漏斗，确保季度/年度销售指标达成； - 跟踪重点国家政策（如欧洲户储补贴、北美新能源税收优惠）、市场趋势，主导竞品分析并提出差异化策略； - 推动跨境多部门协作（产品、技术、供应链），确保客户需求高效响应； - 行业资源整合，挖掘生态合作伙伴（如安装服务商、储能软件服务商），构建本地化合作网络。

Team Introduction： The team primarily focuses on recommendation services for the International E-commerce Mall, covering information flow recommendation in core scenarios such as the mall homepage, transaction funnels, product detail pages, stores & showcases. Committed to providing hundreds of millions of users daily with precise and personalized recommendations for products, live streams, and short videos, the team dedicates itself to solving challenging problems in modern recommendation systems. Through algorithmic innovations, we continuously enhance user experience and efficiency, creating greater user and social value. Project Background/Objectives: This project aims to explore new paradigms for large models in the recommendation field, breaking through the long-standing structures of recommendation models and Infra solutions, achieving significantly better performance than current baseline models, and applying them across multiple business scenarios such as Douyin short videos/LIVE/E-commerce/Toutiao. Developing large models for recommendation is particularly challenging due to the high demands on engineering efficiency and the personalized nature of user recommendation experiences. The project will conduct in-depth research across the following directions to explore and establish large model solutions for recommendation scenarios: Project Challenges/Necessity: The emergence of LLMs in the natural language field has outperformed SOTA models in numerous vertical tasks. In contrast, industrial-grade recommendation systems have seen limited major innovations in recent years. This project seeks to revolutionize the long-standing paradigms of recommendation model architectures and Infra in the recommendation field, delivering models with significantly improved performance and applying them to scenarios like Douyin short video and LIVE. Key challenges include: High engineering efficiency requirements for recommendation systems; Personalized nature of user recommendation experiences; Effective content representation for media formats like short videos and live streams. The project will address these through deep research in model parameter scaling, content/user representation learning, multimodal content understanding, ultra-long sequence modeling, and generative recommendation models, driving systematic upgrades to recommendation models. Project Content: 1. Representation Learning Based on Content Understanding and User Behavior 2. Scaling of Recommendation Model Parameters and computing 3. Ultra-Long Sequence Modeling 4. Generative Recommendation Models Involved Research Directions: Recommendation Algorithms, Large Recommendation Models. 团队介绍： 推荐与营销团队，主要负责国际电商商城推荐业务，涵盖商城首页、交易链路、商品详情页、店铺&橱窗等多个核心场景的信息流推荐业务，致力于每天为亿量级用户提供精准个性化商品、直播、短视频推荐服务；团队致力于解决现代推荐系统中各种有挑战的问题，通过算法不断提升用户体验和效率、创造更大的用户和社会价值。 课题背景/目标： 本项目旨在探索推荐领域下的大模型新范式，突破现在持续了较长时间的推荐模型结构和Infra的方案，且效果大幅好于现在的基线模型，在抖音短视频/直播/电商/头条等多个业务场景上得到应用。推荐领域的大模型是比较有挑战的事情，推荐对工程效率的要求更高，且用户的推荐体验上是个性化的，本课题会以下多个方向来做深入的研究，探索和建设推荐场景的大模型方案。 课题挑战/必要性： 自然语言领域LLM的出现，效果在众多垂直任务上都好于sota模型，从推荐领域看过去工业级推荐系统在较长的时间没有大幅的变化过。本项目旨在探索推荐领域下的大模型方案，改变现在持续了较长时间的推荐模型结构和Infra的基本范式，且效果大幅好于现在的模型，在抖音短视频/直播等多个业务场景上得到应用。但是怎么做好推荐领域的大模型也是一个比较有挑战的事情，推荐对工程效率的要求更高，且用户的推荐体验上是个性化的，以及如何短视频、直播等体裁上做号内容的表征也是需要被解决的问题，这里会从模型参数scaling up、内容和用户的表征学习、内容理解多模态、超长序列建模、生成式推荐模型等多个方向来做深入的研究，对推荐场景的模型做系统性的升级。 课题内容： 1、基于内容理解和用户行为的表征学习； 2、推荐模型参数和算力scaling up； 3、超长序列建模； 4、生成式推荐模型。 涉及研究方向：推荐算法、推荐大模型。

We empower our people to stay resilient and relevant in a constantly changing world. We're looking for people who are always searching for creative ways to grow and learn. People who want to make a real impact, now and in the future. Does that sound like you? Then it seems like you'd make a great addition to our vibrant international team. DAI AIX – AI Acceleration and Exploration, is working on the cutting-edge research of Data Analytics and AI with Siemens global technology network, and consulting, co-creation, data driven applications for the end customers. Research Scientist is to do applied research for Industrial AI applications in the team. We are seeking a Reinforcement Learning (RL) Specialist to lead the design, implementation, and optimization of RL-driven systems for post-training of foundation models. The primary focus of this role is advancing our RL capabilities for real-world applications such as industrial control systems and LLM agents. You will develop cutting-edge algorithms, improve post-training efficiency, and deploy scalable RL solutions in industry. You'll make an impact by • Research on state-of-the-art data analytics & AI technologies on a general range. • Mainly focus on modern foundation model applications in industrial scenarios1. Context engineering for foundation models2. Development of agent systems for industrial applications3. Task-specific model finetuning • Partially work with multi-modal applications • Participating in both internal & external research projects • Assist deployment of customer development/deployment project

Join Siemens Digital Industries for the world of tomorrow! Siemens Digital Industries (DI) is an innovator in industrial automation and digitalization. We seamlessly connect the physical and digital worlds with our Digital Enterprise solutions, with the help of a comprehensive "digital twin" for continuous cycle optimization. At the same time, we leverage unlimited data to empower unlimited opportunities for fast and confident decision-making, injecting acceleration into the transformation and sustainable development of industrial enterprises. Role Purpose： Own, orchestrate, and accelerate Siemens’ most critical customer relationships—transforming them into enduring, profitable, and strategically aligned partnerships that drive growth across all Siemens business units and geographies. You’ll make an impact by： Strategic Partnership Development • Translate advanced customer analytics, market intelligence, and Siemens portfolio roadmaps into a 3–5-year partnership strategy. • Secure explicit alignment between customer C-level objectives and Siemens corporate priorities. • Create and execute long-term development plans (incl. investment cases, co-innovation roadmaps, M&A leads) and adjust them dynamically as markets evolve. Customer Relationship Management & Growth • Establish a multi-tier engagement framework—from plant-floor to C-suite—supported by executive sponsorship on both sides. • Continuously surface latent needs and white-space opportunities (incl. overseas markets) and convert them into qualified pipeline. • Serve as the single, empowered “customer CEO” inside Siemens—advocating customer interests while safeguarding Siemens profitability. Cross-Functional & Multi-Regional Leadership • Select and lead virtual opportunity-acquisition squads (Sales, Verticals, Domains, Supply Chain, Finance, Legal etc.) across Sales Regions; remove roadblocks to flawless implementation. • Drive global alignment on contract renewals, pricing, T&Cs, and risk exposure. • Influence senior Siemens executives to secure resources, executive air-cover, and Board-level visibility for strategic initiatives. Business Growth & Market Intelligence • Monitor macro trends, competitor moves, regulatory shifts, and technology disruptions; distill into quarterly insight briefs that trigger joint pivots or new offerings. • Represent Siemens at flagship industry events, standards bodies, and strategic forums—building reputation and expanding the top-of-funnel. Strategic Key Account Planning & Execution • Perform deep-dive analysis of customer markets, competitive position, and unmet needs; identify scalable product, solution, and service plays. • Maximize account penetration and profitable growth for Siemens with cross-sell/up-sell plays. Pipeline & Opportunity Management • Own the entire opportunity funnel—from ideation to order. • Drive execution through direct teams and all indirect channels (distributors, JV partners, system integrators). Team Orchestration & Customer Engagement • Functionally lead the core account team in partnership with vertical and line management; set individual growth targets tied to customer outcomes. • Maintain sustainable relationships at every hierarchical level, including multi-year succession planning for key contacts. • Demonstrate personal, long-term commitment. Sales Administration & Reporting • Maintain a living Account Business Plan updated regularly. • Ensure CRM hygiene (e.g., Salesforce) with real-time opportunity data, competitive intelligence and risk logs.