Intern, Complex Systems Diagnostics and Prognostics Design, Summer 2026

Job Description

Position Title: Intern, Complex Systems Diagnostics and Prognostics Design, Summer 2026

Summary:
We are seeking a highly motivated and detail-oriented summer intern to join our dynamic Fleet Health Management and Remote Diagnostics team. This critical internship offers a unique opportunity to contribute significantly to the development of a unified framework for Vehicle and Fleet Health Management (VHM/FHM). This strategic initiative is at the forefront of automotive innovation, focusing on advanced predictive diagnostics and intelligent fault management across diverse vehicle platforms. Under the mentorship of senior engineers, the intern will engage in cutting-edge projects and gain invaluable experience in a rapidly evolving field, alongside other related duties.

Essential Duties & Responsibilities:
As an Intern, Complex Systems Diagnostics and Prognostics Design, you will play a pivotal role in advancing our capabilities by:

• Contributing directly to Lucid’s Fleet Health Management (FHM) and Prognostics and Health Management (PHM) initiatives, shaping the future of vehicle diagnostics and predictive maintenance.
• Advancing the development of a comprehensive and unified framework for monitoring and managing vehicle health, extending its reach across individual vehicles and entire fleets.
• System Health Modeling: Developing sophisticated models that accurately represent the health status of various vehicle components, subsystems, and full systems. This involves leveraging diverse data sources including real-time telemetry data, detailed engineering design inputs, and comprehensive software diagnostics.
• Knowledge Base Development: Designing and building a flexible, scalable, and robust diagnostic knowledge base. This essential resource will seamlessly integrate complex vehicle architecture, hardware specifications, and software functionalities, engineered to be applicable across multiple trims and vehicle types.
• AI/ML-Driven Fault Detection: Applying advanced machine learning techniques, with a focus on time-series analysis and anomaly detection, to identify the earliest signs of potential failures, precisely isolate root causes, and recommend proactive mitigation strategies *before• issues can escalate, ensuring optimal vehicle performance and safety.
• Knowledge Graph & LLM Integration: Exploring and implementing innovative uses of knowledge graphs and large language models (LLMs) to significantly enhance fault reasoning capabilities, automate the generation of diagnostic logic, and ultimately improve the explainability, scalability, and efficiency of our health management systems.
• Working closely and collaboratively with technical specialists and system architects to effectively translate intricate engineering knowledge and domain expertise into actionable, high-fidelity diagnostic and prognostic models.

Required Qualifications:
Candidates must possess a strong academic foundation and practical experience in the following areas:

• A strong understanding of AI/ML techniques specifically within the domain of Prognostics and Health Management (PHM), with a clear emphasis on early failure detection, precise fault isolation, and effective mitigation strategies.
• A strong foundation in systems engineering or vehicle architecture, essential for understanding complex vehicle interdependencies.
• Demonstrated experience with data analysis and machine learning, particularly in time-series analysis and anomaly detection.
• Familiarity with core Prognostics and Health Management (PHM) concepts.
• Exposure to knowledge representation techniques, such as knowledge graphs.
• Proficiency in Python or similar programming languages, coupled with experience using relevant AI/ML libraries (e.g., TensorFlow, PyTorch, scikit-learn).
• Excellent written and verbal communication skills, vital for clear technical documentation and team collaboration.
• The ability to work both independently and collaboratively within a fast-paced team environment.
• Field(s) of study: Electrical Engineering, Mechanical Engineering, Computer Science, or a related quantitative field.
• Currently enrolled in a Master’s or PhD degree program at an accredited university.
• Proof of enrollment in the current or upcoming program (MS, PhD) will be required.
• Practical experience with vehicle telemetry systems or signal processing.
• Familiarity with Large Language Models (LLMs) or generative AI frameworks.
• Exposure to semantic modeling or ontology development.

Compensation:
The compensation for this role is $50.00–$70.00 / hr.