Job Title:
Back-End Software Engineer Intern (12 months) - Starting Summer 2026
Project Title: Leveraging Python Extensibility in Applications
About SLB:
We are a global technology company, driving energy innovation for a balanced planet.
At SLB we create amazing technology that unlocks access to energy for the benefit of all. That is our purpose. As innovators, that has been our mission for 100 years. We are facing the world’s greatest balancing act- how to simultaneously reduce emissions and meet the world’s growing energy demands. We’re working on that answer. Every day, a step closer.
Our collective future depends on decarbonizing the fossil fuel industry, while innovating a new energy landscape. It’s what drives us. Ensuring progress for people and the planet, on the journey to net zero and beyond. For a balanced planet.
Our purpose: Together, we create amazing technology that unlocks access to energy for the benefit of all. You can find out more about us on
Location:
Abingdon, Oxfordshire
Description & Scope:
The next generation of applications under development for Reservoir Engineering within SLB are expected to seamlessly integrate within GenAI tooling and existing industry workflows. At the heart of this new application is allowing extensibility via the integration of the Python framework alongside a set of open API's that give access to core functionality of the application.
Responsibilities:
This project would be responsible for proving the early stage integration of our new pre & post processing application and other tooling into a wider workflow by leveraging our initial extensibility framework and API's. Other responsibilities and deliverables will include:
1. Creation of workflow that uses our python to drive our application to perform a set of tasks in an automated way.
2. Investigate the different testing methodologies and frameworks for LLM's under the scenarios in use within SLB Deliverables
3. Produce a report on the options for robust automated testing of LLM's within SLB
4. Demonstrate the integration of one of these testing frameworks into an automated CI/CD pipeline
Learning Outcomes
5. Exposure to and use of LLM's
6. Gain experience of building complex multi-functional reservoir engineering workflows.
Qualifications:
7. Studying a Bacehelors or Masters in Computer Science, Reservoir Engineering, or a related disicipline
8. Python programming skills