About the role
Remote - Saudi
- Principal / Frontier-Level Individual Contributo
- 60% research · 40% production engineering
ABOUT CLIENT
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.
THE ROLE
Seeking an exceptional Principal AI Research Engineer — Energy Systems to help establish
and lead 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 infrastructure.
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.
KEY RESPONSIBILITIES
- Define and lead ambitious research programmes at the intersection of artificial intelligence, applied mathematics, scientific computing, and energy 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 technical roadmap.
TECHNICAL ENVIRONMENT
The exact stack will evolve, and the successful candidate will have significant influence over its
direction.
- Python · PyTorch 2.x · JAX▪ NumPy, SciPy, Pandas and Polars · scikit-learn · XGBoost
And LightGBM
- PyTorch Geometric or equivalent graph-learning frameworks
- Probabilistic programming and Bayesian modelling tools
- Optimization frameworks such as Pyomo, CVXPY, OR-Tools or equivalent
- MLflow for experiment tracking, model lineage and registry management
- Kubeflow Trainer and Kubeflow Pipelines
- Git and modern code-review workflows · CI/CD for AI and scientific-computing workloads
- Data and model versioning · automated evaluation and regression testing
QUALIFICATIONS
- Exceptional expertise in machine learning, deep learning, applied mathematics, scientific computing, statistics, control, optimization, or a closely related discipline.
- A demonstrated record of solving technically difficult AI or computational problems
.▪ Deep proficiency in Python and at least one major deep-learning framework, preferably PyTorch or JAX.
- Strong mathematical foundations.
- Experience designing, training, evaluating, and debugging advanced machine-learning models
.▪ Experience taking research from an initial hypothesis through experimentation, validation, implementation, and operational 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 concepts clearly
.▪ Professional fluency in English.
Source: the employer's own careers page.
