Principal Senior AI Research Engineer

g
ABOUT CLIE

NT
A Saudi technology and engineering company developing advanced systems across artificial intelligence, energy, critical infrastructure, security, and industrial technology.

European expansion will establish a multidisciplinary AI laboratory bringing together advanced AI researchers, mathematicians, energy specialists, AI engineers, and software developers.

Seeking an exceptional Principal AI Research Engineer — Energy Systems to help establi sh andlead the technical direction of its frontier AI research programme. This is not a conventional machine-learning engineering position. The successful candidate will operate between advanced research, scientific computing, energy‑system intelligence, and large-scale AI engineering.
You will investigate new AI methodologies, build experimental systems, validate them against real-world energy data, and lead the transition of successful research into secure, production-grade solutions deployed on private GPU infrastrukture.
You will also serve as a technical leader for growing AI team, mentoring AI researchers and engineers, setting scientific and engineering standards, and helping define the company's long-term AI research roadmap.

• Define and lead ambitious research programmes at the intersection of artificial intelligence, applied mathematics, scientific computing, and energ y systems.
• Develop novel architectures, algorithms, training strategies, and evaluation methodologies for complex energy and industrial problems.
• Research advanced approaches including physics‑informed neural networks, graph neural networks, neural operators, probabilistic modelling, representation learning, reinforcement learning, control‑aware learning, and foundation models for time‑series and sensor data.
• Design scientifically rigorous experiments, benchmarks, ablation studies, and validation protocols.
• Translate successful research into reliable prototypes and production‑grade AI systems.
• Lead and mentor AI research engineers, machine‑learning engineers, and supporting AI engineers.
• Help shape long‑term AI research strategy, compute strategy, and technica l roadmap.

The exact stack will evolve, and the successful candidate will have significant influence over its

ch 2.x – JAX
Num Py, Sci Py, Pandas and Polars – scikit‑learn – XGBoost and LightGBM – PyTorch Geometric or equivalent graph‑learning frameworks – Probabilistic programming and Bayesian mod

elling tools – Optimization frameworks such as Pyomo, CVXPY, OR‑Tools or equivalent – MLflow for experiment tracking, model lineage and registr

y management – Kubeflow Trainer and Kubefl

ow Pipelines – Git and modern code‑review workflows – CI/CD for AI and scientific‑computi

ng workloads – Data and model versioning – automated evaluation and regres

sion testing
Candidates are not expected to have used every listed technology. They must, however, demonstrate deep expertise in the underlying principles and the ability to rapidly evaluate and adopt appro

priate tools.

• Exceptional expertise in machine learning, deep learning, applied mathematics, scientific computing, statistics, control, optimization, or a closely relat ed discipline.
• A demonstrated record of solving technically difficult AI or computati onal problems.
• Deep proficiency in Python and at least one major deep-learning framework, preferably P yTorch or JAX.
• Strong mathematics foundations.
• Experience designing, training, evaluating, and debugging advanced machine-learning models.
• Experience taking research from an initial hypothesis through experimentation, validation, implementation, and operation al deployment.
• Evidence of technical leadership through mentoring, architecture ownership, research leadership, opensource contributions, publications, patents, or delivery of major AI systems.
• Ability to communicate complex scientific and engineering con