KAUST AI/ML Support Analyst 2026: Current Listing

KAUST AI/ML Support Analyst

The position supports KAUST’s research community at the intersection of AI engineering and advanced computing. It is not a conventional business-analytics role: the analyst works with research workloads, model development, HPC infrastructure, scientific domains and end-user technical support.

What the Role Does

The published role description centres on enabling researchers to use advanced AI methods effectively on KAUST’s computational infrastructure.

• Develop and optimise Generative AI models for scientific research.
• Fine-tune foundation models on domain-specific datasets.
• Build and support scalable AI/ML workflows.
• Maintain computational benchmarks and regression tests.
• Provide technical consultation to researchers in climate and weather, bioinformatics, CFD, NLP and multimodal AI.
• Support governance, model/artifact control and secure AI workflows.
• Troubleshoot performance and infrastructure problems.
• Prepare documentation, training material and knowledge-sharing resources.
• Support users through direct consultation and service channels.

Qualifications and Candidate Profile

The job information distributed for this role identifies a Bachelor’s or Master’s-level technical background as relevant. Suitable disciplines include computer science, data science, computational science, artificial intelligence or a closely related field.

A strong candidate should combine machine-learning fundamentals with practical research-computing skills and the ability to support researchers with different levels of technical expertise.

Technical Skills

Key technologies associated with the role include:

• Python;
• PyTorch, TensorFlow, JAX or comparable ML frameworks;
• Generative AI and foundation-model fine-tuning;
• SLURM and/or Kubernetes;
• HPC containers such as Singularity/Apptainer or Podman;
• Linux/Unix and Bash;
• distributed model training and inference;
• data-engineering pipelines for scientific workloads;
• benchmarking, profiling and performance troubleshooting; and
• documentation and user-support practices.

Additional experience with GPU systems, NVIDIA profiling tools, large-scale storage, CI/CD, scientific software stacks or languages such as C/C++, R, Julia or Rust can strengthen the profile where relevant.

How to Apply

1. Open the direct KAUST careers vacancy linked below.
2. Confirm that the posting is still active and note the current closing date shown by KAUST.
3. Match your CV explicitly to Python/ML, HPC, Linux, container and research-support requirements.
4. Use project evidence that shows what you built, optimised or troubleshot rather than listing technologies without context.
5. Complete the official KAUST application and retain your submission confirmation.

The role-specific salary is not publicly confirmed in the accessible official source. Do not rely on salary estimates published by third-party job sites.

How to Prepare for Technical Screening

• Prepare an example of diagnosing slow distributed training.
• Be ready to explain GPU utilisation and model-performance profiling.
• Review how you would package an AI workload for an HPC environment.
• Prepare a SLURM or Kubernetes troubleshooting example.
• Explain a foundation-model fine-tuning workflow and its data pipeline.
• Show how you document solutions for researchers with different technical skill levels.

Because this is a support-facing research-computing role, communication and patient problem-solving are part of the technical fit, not separate soft skills.