At a Glance
- Tasks: Join a winning team to develop ML & AI methodologies for the Green Energy transition.
- Company: GE Vernova, a global leader in electrification systems and energy efficiency.
- Benefits: Collaborative culture, diverse environment, and opportunities for professional growth.
- Other info: Mentor a team while working on innovative projects in a dynamic setting.
- Why this job: Make a real impact on engineering design processes with cutting-edge technology.
- Qualifications: Post-graduate degree in Computer Science or related field; strong programming skills required.
The predicted salary is between 72000 - 88000 £ per year.
Become part of a winning team and help to deliver the Green Energy transition. This position will be responsible for developing ML & AI methodologies.
Main focus of the role:
- APOW (Automated Process and Optimisation Workbench) - tool reconstruction, develop this tool and develop new ideas.
- Programming
- Machine Learning & AI
- Mentoring and supervising a team of 4
GE Vernova is a multi-national organisation, employing over 80,000 people worldwide. In the GE Vernova Gas Power Analytics & Digital engineering team, based in Rugby, we are constantly evaluating methods and algorithms that offer the opportunity to improve the efficiency and effectiveness of our engineering design and analysis tools and processes. Our main area of interest is automation and optimisation of engineering design processes. We enjoy a multi-cultural working life at GE Vernova, and promote a friendly and productive working environment.
Roles and Responsibilities:
- We require a computer science engineer. This role will include the study of ML-AI techniques for our engineering applications. The scope of work is expected to be, but not limited to:
- AI Engineer and Machine Learning focus
- APOW (Automated Process and Optimisation Workbench) - tool reconstruction, develop this tool and develop new ideas.
- Mentoring and supervising a team of 4
- Design, develop, test, and deploy clean, modular, and well-documented pipelines to build machine learning components, integrating them into the in-house platform to support the adoption of AI tools by engineers.
- Maintain and enhance existing machine learning capabilities, including data management, computer vision and regression tools.
- Translate stakeholder challenges and requirements into actionable, effective solutions.
- Stay up to date with the latest advancements in AI techniques and tools to ensure the application of state-of-the-art solutions.
Qualifications:
- Post-graduate degree in Computer Science, Statistics, Machine Learning or Data Science.
- Advanced and demonstrated experience in:
- Solid foundation of Digital and Web methodology implementation.
- Understanding of MLOps principles and practices.
- Significant Java programming skills, including Swing and Java-FX.
- Experience in Linux & Windows operating systems.
- Good communication skills.
- Good, adaptable team worker, eager to learn and develop.
- Ability and confidence to work independently.
This role involves work for customers who manage critical infrastructure; based on customer requirements it will be necessary to carry out background checks and suitability assessments (e.g. UK Security Clearance (SC)) as part of the hiring process.
Hands-on experience with (or similar tools) APOW: APOW (Automated Process and Optimization Workbench) is an in-proprietary software tool. It allows design and engineering teams to build automated computational workflows, minimise human error, and perform advanced multi-variable optimisation for complex engineering systems.
Core Functions and Capabilities:
- Workflow Automation: Standardise routine calculation and simulation tasks (such as Computational Fluid Dynamics or CFD) to capture institutional knowledge.
- Mathematical Integration: Connects established workflows with math routines to evaluate system performance.
- Design Exploration: Runs Design of Experiments (DoE) to test how adjustments in shape or parameters affect overall output.
- Optimisation & Machine Learning: Integrates optimisation algorithms and surrogate modelling (like Kriging models) to accelerate design enhancements and uncertainty quantification (UQ).
Typical Engineering Applications:
- Used heavily in turbomachinery and power generation design.
- Optimises physical components like turbine endwalls, casings, and stators to improve aerodynamic efficiency and reduce emissions.
About Us:
GE Vernova’s Power Conversion & Storage business provides electrification systems that are critical to customers’ power and energy needs for their high-performance applications. We work with some of the world’s major energy, maritime and industrial organisations, helping to enable a transition to energy efficiency and decarbonisation, including through our specialist motors, power electronics systems, electrical drives and control technologies that evolve today’s industrial processes for a cleaner, more productive future.
GE’s Power Conversion business, part of GE Vernova, provides electrification systems that are critical to customers’ power and energy needs for their high-performance applications. We work with some of the world’s major energy, maritime and industrial organisations, helping to enable a transition to energy efficiency and decarbonisation, including through our specialist motors, drives and control technologies.
Our engineers dedicated to Naval get involved in solving exciting challenges through leading the development and delivery of the engineering aspects of our projects. Projects that range from designing, de-risking and delivery of new power / propulsion and energy system solutions alongside digital & automation technologies to our UK and international customers both to surface and submarine systems.
Research and development are key to what we do, and you will have the opportunity to influence and play a critical role in the successful execution of programs for future surface ships, submarines and future technology programs along with the growth of a unique world-class facility, its optimised operations and the development of new capabilities, tools and processes.
Relocation Assistance Provided: No
Machine Learning & Artificial Intelligence Engineer in Rugby employer: GE Vernova
GE Vernova in Stafford is an exceptional employer, offering a dynamic work culture that fosters innovation and collaboration among engineers. With a strong commitment to employee growth, you will have access to ongoing training and development opportunities, as well as the chance to work on cutting-edge HVDC projects that make a real impact globally. The supportive environment and emphasis on teamwork ensure that every employee can thrive while contributing to sustainable energy solutions.
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We think you need these skills to ace Machine Learning & Artificial Intelligence Engineer in Rugby
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