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As we transition to cleaner forms of energy, the challenge facing the energy industry in the coming decades is considerable. AI is playing a crucial role in navigating through the uncertainties of the energy future. In this role, you will contribute to the AI innovation within the company.

As an innovation organization, we maintain a portfolio of digital projects driven by applied research, new product innovation, product contribution and infrastructure goals. As an AI Researcher, you will setup large-scale experiments and deploy promising ideas quickly and broadly, managing deadlines and deliverables while applying the latest theories to develop new and improved products, processes, or technologies. From creating proof of concepts and prototyping implementations to designing new architectures, our AI Researchers work on real-world problems that span the breadth of various lines of business. By leveraging geospatial data and advanced AI, you will help generate actionable insights about the Earth and the built environment, supporting cleaner, more efficient, and more sustainable energy transition pathways. As an AI Researcher, you'll also actively contribute to the wider Shell.ai community by sharing and publishing your findings, with ideas inspired by internal projects as well as from collaborations with strategic partners, universities, and technical institutes all over the world.

If you want to learn more about Shell.ai, check out this link –

https://www.shell.com/energy-and-innovation/digitalisation/digital-technologies/shell-ai.html

What’s the role –

You will be responsible for innovation, concept development and productizing of AI/deep learning applications with direct business impact. More specifically, responsibilities in your role includes:

• Expected to be a key technical researcher/developer on deep learning and AI projects
• Develop technical proposals and research activities for applied AI across Shell’s businesses.
• Lead applied research and prototyping in geospatial AI, including geospatial data processing, agentic AI for geospatial use cases, and self-supervised learning approaches.
• Consult stakeholders in technical feasibility/data quality/models for their problems.
• Use innovative research to develop novel technologies from ideation to deployment.
• Drive solutions to commercial value, and collect feedback from end-users.
• Design novel AI algorithms and incorporate emerging AI technologies into our applications.
• Engage with internal and external stakeholders (partners/universities), and communicate technical approaches/results to a general audience.
• Analyze real-world engineering & scientific datasets, and explore their use for AI solutions
• Implement and optimize data I/O pipelines and training/inference scripts

Requirements:

• Ph.D. or Master’s degree in computer science, engineering, mathematics, theoretical science, statistics, or related scientific field
• Demonstrated track record of hands-on experience building and experimenting with geospatial AI solutions and geospatial data processing pipelines.
• Experience applying agentic AI for geospatial purposes/use cases, including the design of autonomous or semi-autonomous workflows for spatial reasoning, analysis, and decision support.
• Experience using self-supervised learning methods in computer vision, geospatial AI, or multimodal/spatiotemporal learning settings.
• 3+ years of experience with PyTorch (or TensorFlow or Keras)
• 3+ years of experience in AI related domains, especially Computer Vision applications
• Hands-on coding skills in Python and software development skills
• Hands-on experience with geospatial tools such as QGIS and Python libraries including GeoPandas and TorchGeo
• Proven examples of implementing experimental pipelines and prototyping AI applications
• A mathematical background covering some subset of linear algebra, probability, multivariate calculus, geometry, and/or numerical methods
• Awareness of lifecycle and data management, and experience using Git to manage models, and source code
• Experience with cloud stacks and architectures, such as Azure, AWS, etc. Comfort with some flavor of Unix shell environment (e.g., bash)
• Publications in AI/scientific journals or conferences is a plus