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职位描述
特斯拉为信息技术部开放 IT MFG DevOps AI 全职岗位(工作地点:特斯拉上海超级工厂)。若你是融合 AI 开发、DevOps 实践与制造业技术的全能专家,能在智能制造场景下高效应对挑战、解决复杂技术问题,拒绝重复低效的工作模式,那么该岗位正适合你。

IT MFG DevOps AI 是连接公司 IT 系统与生产制造环节的核心角色,身处智能制造落地的一线。你将每日对接 AI 技术研发、容器化部署与生产运维等多领域工作,通过技术实践支持公司优化生产流程、提升制造效率,助力实现智能制造转型的核心目标。

岗位职责

  • 负责 AI 算法研发、模型优化与训练,聚焦生产线数据分析、质量控制、故障检测、自动化生产等场景,确保 AI 技术适配制造业务需求。
  • 基于 Kubernetes(K8s)与 Docker 容器技术,完成 AI 解决方案的部署、监控与扩展,保障生产环境中系统的高可用性与稳定性。
  • 参与 DevOps 流程建设,优化 AI 模型与系统的开发、测试、部署全链路,实现自动化部署、持续集成(CI)与持续交付(CD)。
  • 与生产、质量控制、研发等制造相关部门对接,深入理解业务痛点,提供数据驱动的 AI 技术解决方案。
  • 快速响应生产线上的技术需求与故障,排查 AI 系统、容器集群、网络环境等问题,减少对生产进度的影响,提升生产效率与质量。
  • 跟踪 AI 与 DevOps 领域前沿技术(如工业大模型、云原生运维)及行业动态,推动新技术在制造场景的预研与应用,持续优化系统性能。

任职要求
必备条件

  • 计算机科学、人工智能、软件工程、电子工程等相关专业。
  • 工作经验:至少 2 年 AI 开发、DevOps 或自动化部署相关工作经验,有制造业(尤其汽车制造、工业制造)相关经验者优先。
  • 技术能力:
  • 熟练掌握 Python 语言,可独立编写高效的 AI 算法代码、数据处理脚本与 DevOps 自动化脚本,同时能运用 Dify 等低代码 AI flow 工具,在低代码环境中快速调用大模型o 以满足制造场景下的业务需求。
  • 精通 Kubernetes(K8s)与 Docker 技术,具备生产环境下容器化部署、集群管理与运维的实战经验。
  • 熟悉机器学习框架,能独立完成制造场景下 AI 模型的设计、训练与优化。
  • 了解 Hadoop、Spark 等大数据处理技术,可处理制造过程中产生的海量生产数据(如传感器数据、日志数据)。
  • 熟悉 CI/CD 流程与工具链(如 Jenkins、GitLab CI),能使用 Terraform、Ansible 等工具实现自动化构建、测试与部署。
  • 掌握 SQL 与 NoSQL 数据库(如 MySQL、MongoDB、PostgreSQL),可实现制造数据的高效存储与查询。
  • 软技能:
  • 具备强烈的自我驱动力,能在较少监督下独立规划并完成工作任务。
  • 拥有出色的沟通能力,可顺畅对接技术与非技术团队,清晰传递需求与解决方案。
  • 具备优秀的问题分析与解决能力,能快速定位生产线中的 AI 系统、DevOps 链路故障并解决。

优先考虑

  • 有汽车制造或工业制造行业工作经验,熟悉冲压、焊接、总装等生产流程者优先。
  • 具备机器人技术、物联网(IoT)(如工业传感器数据采集、边缘计算)应用经验者优先。
  • 熟悉敏捷开发流程,有参与或主导敏捷项目经验者优先。
  • 持有 Kubernetes 认证(如 CKA、CKAD)、云厂商认证(如 AWS Certified DevOps Engineer)者优先。

The Role

TESLA is offering a full-time IT MFG DevOps AI position in the Information Technology Department (Work Location: Tesla Giga Factory Shanghai). If you are a versatile expert integrating AI development, DevOps practices, and manufacturing technology—someone who can efficiently tackle challenges, solve complex technical problems in smart manufacturing scenarios, and reject repetitive and inefficient work patterns—this role is perfect for you.

IT MFG DevOps AI is a core role connecting the company's IT systems and manufacturing processes, standing at the forefront of smart manufacturing will engage in work across multiple domains, including AI technology R&D, containerized deployment, and production operations. Through technical practice, you will support the company in optimizing production processes, improving manufacturing efficiency, and contributing to the core goal of smart manufacturing transformation.

Responsibilities

  • Undertake AI algorithm R&D, model optimization, and training, focusing on production line scenarios such as data analysis, quality control, fault detection, and automated production to ensure AI technology aligns with manufacturing business needs.
  • Complete the deployment, monitoring, and scaling of AI solutions based on container technologies like Kubernetes (K8s) and Docker, ensuring high availability and stability of the system in the production environment.
  • Participate in DevOps process development, optimize the full lifecycle of AI model and system development, testing, and deployment, and realize automated deployment, continuous integration (CI), and continuous delivery (CD).
  • Collaborate with manufacturing-related departments such as production, quality control, and R&D to deeply understand business pain points and provide data-driven AI technical solutions.
  • Respond quickly to technical requirements and faults on the production line, troubleshoot issues in AI systems, container clusters, and network environments, minimize impacts on production progress, and improve production efficiency and quality.
  • Track cutting-edge technologies in the AI and DevOps fields (e.g., industrial large models, cloud-native operations) and industry trends, promote the pre-research and application of new technologies in manufacturing scenarios, and continuously optimize system performance.

Requirements

  • Educational Background: Bachelor's degree or above in relevant fields such as Computer Science, Artificial Intelligence, Software Engineering, or Electronic Engineering. Familiar with low code AI platform.
  • Work Experience: At least 2 years of experience in AI development, DevOps, or automated deployment; experience in the manufacturing industry (especially automotive manufacturing or industrial manufacturing) is preferred.
  • Technical Competencies:
  • Proficiency in the Python programming language, capable of independently writing efficient AI algorithm code, data processing scripts, and DevOps automation scripts.
  • Mastery of Kubernetes (K8s) and Docker technologies, with practical experience in containerized deployment, cluster management, and operations in production environments.
  • Familiarity with machine learning frameworks, able to independently design, train, and optimize AI models for manufacturing scenarios.
  • Understanding of big data processing technologies such as Hadoop and Spark, capable of processing massive production data generated during manufacturing (e.g., sensor data, log data).
  • Familiarity with CI/CD processes and toolchains (e.g., Jenkins, GitLab CI), and ability to use tools like Terraform and Ansible to implement automated building, testing, and deployment.
  • Proficiency in SQL and NoSQL databases (e.g., MySQL, MongoDB, PostgreSQL) to enable efficient storage and querying of manufacturing data.
  • Soft Skills:
  • Strong self-motivation, able to independently plan and complete work tasks with minimal supervision.
  • Excellent communication skills, capable of smoothly collaborating with both technical and non-technical teams, and clearly conveying requirements and solutions.
  • Outstanding problem analysis and solving abilities, able to quickly identify and resolve faults in AI systems and DevOps workflows on the production line.

Preferred Qualifications

  • Work experience in the automotive manufacturing or industrial manufacturing industry, with familiarity with production processes such as stamping, welding, and final assembly is preferred.
  • Experience in robotics technology or IoT (Internet of Things) applications (e.g., industrial sensor data collection, edge computing) is preferred.
  • Familiarity with agile development processes and experience in participating in or leading agile projects is preferred.
  • Holders of Kubernetes certifications (e.g., CKA, CKAD) or cloud service provider certifications (e.g., AWS Certified DevOps Engineer) are preferred.
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