Lead Industrial AI & Optimization Engineer

Do you enjoy applying machine learning, advanced process control, and optimization to solve complex industrial challenges?

Would you like to help develop digital products that improve the performance, reliability, and efficiency of energy and industrial assets?

As the Lead Industrial AI & Optimization Engineer, you will be responsible for:

• Developing machine learning and hybrid modeling solutions for industrial process applications, including neural networks, recurrent neural networks, convolutional neural networks, transformer-based architectures, and time-series models

• Utilizing industrial process engineering, advanced process control, and real-time optimization domain knowledge to develop robust machine learning algorithms

• Evaluating model performance, uncertainty, robustness, maintainability, and scalability in industrial deployment environments 

• Supporting integration of machine learning models with optimization workflows, advanced process control systems, industrial data historians, cloud platforms, and enterprise software architectures

• Designing, training, validating, and deploying models using Python and associated scientific, machine learning, and software development libraries. 

• Helping define best practices for machine learning model lifecycle management, including data preparation, feature engineering, experiment tracking, model governance, monitoring, and continuous improvement

• Collaborating with product managers, software engineers, process engineers, data scientists, UX teams, and customer-facing teams to deliver features aligned with product roadmaps and customer needs. 

To be successful in this role you will have:

• A Bachelor’s or Master’s degree (in Chemical Engineering, Process Systems Engineering, Control Engineering, Industrial Engineering, Computer Science, Data Science, Applied Mathematics, or a related technical discipline)
• Experience developing machine learning models for engineering, industrial, time-series, or process systems applications
• Knowledge of process modeling, dynamic simulation, advanced process control, model predictive control, real-time optimization, or process optimization methods.
• Strong Python programming skills and experience with common scientific and machine learning libraries
• Practical experience with neural network architectures, including feedforward neural networks, RNNs, LSTMs or GRUs, CNNs, transformer models, and sequence modeling approaches
• Ability to comfortably work across disciplines, including process engineering, data science, control systems, software engineering, cloud architecture, and customer-facing product teams
• Demonstrated strong analytical thinking, communication skills, technical documentation discipline, and ability to explain complex modeling concepts to technical and non-technical stakeholders
• Prior experience in energy, oil and gas, LNG, refining, petrochemicals, power generation, or industrial automation applications; this would be an added advantage

Work in a Way That Works for You

We recognize that everyone is different and that the way people want to work and deliver at their best is different for everyone too. In this role, flexible working patterns may be available depending on business needs, role requirements, and local policies. The role is open to remote working in the US.

Working with us

Our people are at the heart of what we do at Baker Hughes. We know we are stronger when our people are developed, engaged, and empowered to bring their authentic selves to work. We invest in wellbeing, capability development, and leadership growth across all levels.

Working for you

Our innovations have transformed the energy industry for over a century. To continue progressing, we look for individuals who embrace change and contribute to building the future.

What You Can Expect

• Flexible working opportunities
• Contemporary work-life balance policies and wellbeing activities
• Comprehensive private medical care options
• Safety net of life insurance and disability programs
• Tailored financial programs
• Education assistance
• Generous parental leave
• Mental health resources
• Dependent care support
• Additional elected or voluntary benefits