Student Intern

SLB • Sugar Land, TX

10d

About The Position

Development of next-generation engineering workflows that combine domain expertise with Artificial Intelligence (AI). The role focuses on connecting mechanical material property databases, engineering standards, qualification records, and critical mechanical component design and life calculators with SLB AI Tools and agents.

Requirements

• Pursuing a degree in Mechanical Engineering, Materials Engineering, Engineering Data Science, or related discipline.
• Strong understanding of materials science, mechanical engineering fundamentals, and engineering calculations.
• Strong analytical, problem-solving, and communication skills.

Nice To Haves

• Exposure to Microsoft Copilot, ChatGPT, Azure OpenAI, or GitHub Copilot.
• Familiarity with Python, SQL, Power BI, APIs, and data integration concepts.
• Knowledge of LLMs, AI Agents, Agentic Workflows, RAG, Vector Databases, and Semantic Search.
• Exposure to seal design principles, elastomeric materials, and industrial applications.

Responsibilities

• Support materials and mechanical engineering teams in organizing and validating engineering data.
• Assist in integrating engineering databases, material property data, and seal design calculators with AI-powered applications.
• Support development of AI agents, copilots, and RAG solutions for engineering knowledge retrieval and decision support.
• Collaborate with subject matter experts to structure engineering knowledge for AI applications.
• Evaluate and validate AI-generated engineering recommendations.
• Prepare, cleanse, and structure engineering datasets for AI workflows.
• Develop data mappings between engineering systems, calculators, and AI platforms.

What This Job Offers

• Career Level: Intern