Software Engineer Intern – Subsurface ML for Energy

SLB; Abingdon; On-site; GBP 30,000 - 39,000; Full time

Job summary

SLB in Abingdon, UK, is offering a Software Engineer Intern (6 months) starting Summer 2027. You’ll join a team building software for monitoring CO2 storage and geothermal projects at the intersection of subsurface science, ML, and energy transition.

Work will include deploying surrogate ML models, developing data-visualization components for engineers, and instrumenting end-to-end pipelines to ensure accuracy, speed, and production readiness, with a focus on reliability and monitoring.

Qualifications

• Studying a Bachelors or Masters in Computer Science or a related field.
• Backend / MLOps - building services and automated deployment pipelines; model versioning and execution.
• Frontend - building data-visualization components and interactive views.
• Software Testing - automated testing, performance measurement, and reliability/monitoring.
• Machine Learning (desirable) - a working understanding of ML models is a plus.

Responsibilities

• Surrogate model deployment & execution for subsurface monitoring - taking fast, data-driven models that track CO2 plume migration or thermal fronts and running them end-to-end in production: model versioning, automated deployment through pipelines, model execution, and ongoing monitoring.
• Designing intuitive components that turn complex subsurface parameters, correlated with surface measurements, into clear, actionable views for engineers. Reliability & performance monitoring.
• Instrumenting the end-to-end solution to measure its accuracy, speed, and robustness, and helping make it production-ready.

Skills

Backend/MLOps

Frontend

Software Testing

Machine Learning

Education

Bachelors or Masters in Computer Science or related field

Job description

SLB in Abingdon, UK, is offering a Software Engineer Intern (6 months) starting Summer 2027. You’ll join a team building software for monitoring CO2 storage and geothermal projects at the intersection of subsurface science, ML, and energy transition.

Work will include deploying surrogate ML models, developing data-visualization components for engineers, and instrumenting end-to-end pipelines to ensure accuracy, speed, and production readiness, with a focus on reliability and monitoring.