What to Expect

The Manufacturing Quality Data Engineering team specializes in predicting safety critical issues before they occur, protecting our customers. As part of Tesla's Vehicle Engineering organization, you'll have access to the data gold mine of design, manufacturing, and vehicle data sources, enabling you to design, develop, and deploy innovative generative AI and machine learning platforms globally. In this role, you'll focus on developing the backend of our generative AI systems, including agentic data pipelines and integrations.

What You'll Do

• Design and deploy scalable architecture for AI applications, including generative systems, using LangGraph and ensuring seamless integration of components to improve overall system performance
• Develop and refine AI and machine learning models, including graph-based models, using technologies like Large Language Model, Retrieval Augmented Generation, TensorFlow, PyTorch, and scikit learn to create intelligent systems that drive business valueCreate and iterate on a comprehensive evaluation framework for generative models, focusing on performance improvements and optimization
• Architect and implement data pipelines and automation systems to streamline data retrieval, processing, analysis, and visualization, ensuring efficient handling of batch and real time data
• Design and implement robust data storage and management systems, leveraging relational databases, NoSQL databases, and graph databases to support diverse data needs
• Extract actionable insights from manufacturing, equipment, and vehicle data to predict and prevent failures, informing proactive maintenance and quality control strategies
• Develop and integrate back-end APIs, iterating on payload and response designs to ensure seamless interaction with systems and applications, and optimizing overall system interoperability

What You'll Bring

• Bachelor's Degree in Computer Science, Information Technology, or a related field, or equivalent experience
• 3+ years of work experience in software development
• Experience with software engineering technologies, including Python, Docker, Kubernetes, MySQL, Microsoft SQL, MongoDB, Elasticsearch, Apache Kafka, and ETLs
• Experience with machine learning frameworks and technologies, including Large Language Model, Retrieval Augmented Generation, LangGraph, TensorFlow, PyTorch, scikit learn, and graph neural networks
• Strong attention to details and diligent in unit testing and integration testing practices
• Experience working in a fast-paced environment with many different projects

Compensation and Benefits