Jackson Green Recruitment Limited
Principal Senior AI Research Engineer
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Principal AI Research Engineer — Energy
Remote
Principal / Frontier-Level Individual Contributor
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 a 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.
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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
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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 appropriate tools.
REQUIRED 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, open-source contributions, publications, patents, or delivery of major AI systems.
- Ability to communicate complex scientific and engineering concepts clearly.
- Professional fluency in English.
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