AMDSr. Field Applications Engineer

KEY RESPONSIBILITIES Collaborate with the AMD Sales team and multinational internal Engineering & BU teams to seek design-in opportunities and provide the technical support to ensure sell-through targets. Location Position is in Beijing

THE ROLE: Join the AMD AECG (Adaptive and Embedded Computing Group) as the leader of our China Customer Engineering team to further strengthen and grow the team. In this role, you will lead the customer program engagements and deep customer co-engineering supporting Embedded x86 customers in the Greater China market. In this customer-facing role, you will collaborate with local FAE and sales managers, global Customer Applications Engineering teams and R&D Engineering teams, and many other cross-functional stakeholders to ensure successful, on-time and high-quality deployment of AMD Embedded x86 processors into customer designs from evaluation through development and production. You will also build strong and deep relationships with engineering leaders of the customers and be the influential voice of customer internally. Key market segments are networking, storage, automotive and edge-AI. THE PERSON: Brief description of what type of person would be successful in the role and key traits needed KEY RESPONSIBILITIES: Team Leadership: Lead a team of local Customer Application Engineers and other technical experts who may be remote to engage with China customers to adopt and develop designs with AMD Embedded x86 processors. Build and grow the Greater China Customer Engineering team through hiring and team development. Evaluations and Design-Wins: Engage yourself and team deeply with customers to understand the key care-abouts, enable hands-on evaluations and build compelling technical and architectural engagement to win China customer designs working closely with global teams Issue Resolution / Customizations: Oversee the triage, debugging, and resolution of customer issues, ensuring timely coordination with internal engineering and product teams, and drive issue closure. Build strongly technical team to create and deliver custom features in self-contained fashion. Escalation and Crises Management: Serve as the primary escalation point for complex customer engineering challenges, driving resolution and customer satisfaction. Technical Guidance: Provide training and support to customers and ODMs to adopt AMD Embedded x86 processors, development tools and design guides. Customer Communication: Drive the team to create and review technical information disclosure, training materials, and other customer-facing documentation. Resource & Onboarding Management: Direct hardware resources allocation, continue to manage, develop and grow a high-performing Customer Engineering team in China. Build strong competent team with the key expertise needed for emerging markets. Deep Partnership and Co-engineering with Customers : Built a customer obsessed team of strong technical engineers who can work in deep co-engineering working model with customers and this partnership building a competitive moat.

特斯拉数据算法团队在工业智能研发中扮演关键角色。我们通过自主构建数据算法平台，赋能生产制造、供应链、销售、服务和充电网络等业务领域，将海量信息转化为高价值数据资产，从而打造更卓越的产品并提升用户体验。 作为特斯拉数据算法工程师，您将全程参与自研数据算法产品和项目的孵化、落地与迭代过程。从数据收集、清洗和预处理，到模型训练与生产部署，您将主导整个流程。理想候选人应热爱人工智能，并紧跟领域前沿动态。 本职位聚焦工业领域的计算机视觉应用，包括缺陷检测、视觉引导、尺寸测量以及视觉大模型等。 职责描述 负责对接公司内部计算机视觉项目，独立设计视觉方案、部署落地，并管理项目全生命周期。 处理计算机视觉项目的图像收集、整理、过滤和清洗；执行数据预处理、模型训练、迭代、重训练，以及准确率优化和模型搜索等任务，涵盖分类、识别和图像分割等领域。 探索多模态大模型在工业场景的应用，研究少样本检测、视频理解等方向的创新解决方案。 追踪计算机视觉技术前沿趋势，提出创新方案应对工业生产挑战。 必备条件 计算机科学、数学、统计学或相关学科的本科及以上学历。 扎实的Python和C++开发经验。 精通OpenCV等图像处理算法。 具备TensorFlow或PyTorch模型开发经验。 掌握数据科学工具，如Pandas、NumPy、Matplotlib，以及MongoDB Aggregation等。 有多模态大模型相关项目经验，并在至少一个领域（如多模态大模型、多模态表征或少样本学习）有深入研究。 优先条件 有将计算机视觉技术应用于工业制造或相关领域的实际项目经验。 熟悉机器人/PLC控制、工业相机/激光传感器/光源解决方案。 有在敏捷开发环境中的工作经验。 具备优秀的书面和口头沟通能力。 有项目管理经验，能按时节点完成开发任务。 拥有算法开发背景，例如参与过ACM竞赛。 在相关领域的学术期刊或会议上发表过论文。 加入我们 加入特斯拉，您将在充满活力和创新的环境中，与全球顶尖工程师和科学家合作，通过机器视觉技术推动工业自动化和智能制造的进步。如果您对机器学习、人工智能和计算机视觉充满热情，并渴望在这一前沿领域实现自我价值，欢迎成为我们的一员！ The Role Tesla's Data Algorithms Team plays a pivotal role in industrial intelligence research and development. We empower various business areas—including manufacturing, supply chain, sales, service, and charging networks—by building our own data algorithms platform. This transforms vast amounts of information into high-value data assets, enabling us to create superior products and deliver an enhanced user experience. As a Tesla Data Algorithms Engineer, you will be fully involved in the incubation, implementation, and iteration of our in-house data algorithms products and projects. From data collection, cleaning, and preprocessing to model training and production deployment, you will lead the entire process. The ideal candidate is passionate about artificial intelligence and stays abreast of the latest developments in the field. This position focuses on computer vision applications in the industrial sector, including defect detection, visual guidance, dimension measurement, and large vision models. Responsibilities Handle internal computer vision projects, independently design visual solutions, deploy them, and manage the full project lifecycle. Manage image collection, organization, filtering, and cleaning for computer vision projects; perform data preprocessing, model training, iteration, retraining, accuracy optimization, and model search tasks, covering areas such as classification, recognition, and image segmentation. Explore the application of multimodal large models in industrial scenarios, researching innovative solutions in directions like few-shot detection and video understanding. Track cutting-edge trends in computer vision technology and propose innovative solutions to address challenges in industrial production. Required

Technical Development & Architecture * Design and implement scalable AI/ML solutions for Compliance use cases * Lead the development of efficient ML models and end‑to‑end data processing pipelines from ingestion to serving. * Build robust, production-grade AI services using Python and modern ML frameworks. * Make and document sound architectural decisions, ensuring systems are scalable, secure, and cost‑effective. * Establish and maintain high engineering standards, including testing, monitoring, and documentation. Engineering Leadership * Partner closely with data scientists, product managers, and operations teams to deliver end‑to‑end AI/ML solutions. * Define and evolve the technical architecture for AI-powered features and platforms. * Lead code reviews, enforce best practices, and elevate engineering quality across the team. * Continuously improve AI system performance, reliability, and latency through experimentation and optimization. Technical Collaboration & Operations * Work with cross‑functional partners to understand requirements, refine scope, and prioritize technical work. * Provide technical guidance and mentorship to junior and mid‑level engineers. * Collaborate with platform and DevOps teams to ensure smooth deployment, monitoring, and maintenance of AI systems. * Implement and evolve ML Ops practices (e.g., CI/CD for models, feature stores, model monitoring, and retraining workflows).