特斯拉Sr. AI DevOps Engineer，IT Support

任职要求

• Educational Background: Bachelor’s degree or above in relevant fields such as Computer Science, Artificial Intelligence, Software Engineering, or Electronic Engineering.
• Work Experience: At least 3 years of experience in AI development, DevOps, or automated deployment; experience in user support or IT operations (especially in tech or service-oriented environments) is preferred.
• Technical Competencies:
o Proficiency in the Python programming language, capable of independently writing efficient AI algorithm code, data processing scripts, and DevOps automation scripts, with expertise in implementing FT, SFT, and RL algorithms.
o Mastery of Kubernetes (K8s) and Docker technologies, with practical experience in containerized deployment, cluster management, and operations in production environments, supporting AI models tuned for real-time user interactions.
o Familiarity with machine learning frameworks (e.g., TensorFlow, PyTorch), able to independently design, train, and optimize AI models for user support scenarios, including advanced tuning techniques and underlying technologies like neural networks, Transformer architectures, attention mechanisms, and model compression.
o 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 of AI systems with integrated tuning pipelines.
o Proficiency in SQL and NoSQL databases (e.g., MySQL, MongoDB, PostgreSQL) to enable efficient storage and querying of user support data, facilitating AI model training and reinforcement learning feedback loops.
• Soft Skills:
o Stro…

工作职责

The Role

TESLA is offering a full-time IT Support 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—someone who can efficiently tackle challenges, solve complex technical problems in user support and experience scenarios, and reject repetitive and inefficient work patterns—this role is perfect for you.
IT Support DevOps AI is a core role connecting the company’s IT systems and user-facing processes, standing at the forefront of enhanced user support implementation. You will engage in work across multiple domains, including AI technology R&D, containerized deployment, and operational support. Through technical practice, you will support the company in optimizing user interactions, improving support efficiency, and contributing to the core goal of user experience transformation.

Responsibilities

• Undertake AI algorithm R&D, model optimization, and training, with a strong emphasis on fine-tuning (FT), supervised fine-tuning (SFT), reinforcement learning (RL), and advanced tuning techniques; focus on user support scenarios such as data analysis, query resolution, issue detection, and automated assistance to ensure AI technology aligns with user experience 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 operational environment, while integrating AI underlying technologies like neural networks and Transformer architectures for efficient performance.
• 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), incorporating RL-based optimization and model tuning for adaptive user support systems.
• Collaborate with user support-related departments such as helpdesk, customer service, and product teams to deeply understand user pain points and provide data-driven AI technical solutions, leveraging SFT and attention mechanisms to enhance personalized user experiences.
• Respond quickly to technical requirements and faults in user-facing systems, troubleshoot issues in AI systems, container clusters, and network environments, minimize impacts on user interactions, and improve support efficiency and satisfaction through advanced AI tuning and underlying model diagnostics.
• Track cutting-edge technologies in the AI and DevOps fields (e.g., large language models with FT/SFT/RL integration, cloud-native operations) and industry trends, promote the pre-research and application of new technologies in user support scenarios, and continuously optimize system performance using techniques like model compression and quantization.