AI/ML Support Analyst

AI/ML Support Analyst Job Details | King Abdullah University of Science & Technology Skip to main content Home Professional Life at KAUST Living at KAUST Career Areas Academic and Research Jobs Innovation and Entrepreneurship Jobs Administration and Support Services Jobs Engineering and Facilities Management Jobs The KAUST School Jobs Elevate Program Join our Talent Community Search by Keyword Home Professional Life at KAUST Living at KAUST Career Areas Academic and Research Jobs Innovation and Entrepreneurship Jobs Administration and Support Services Jobs Engineering and Facilities Management Jobs The KAUST School Jobs Elevate Program Join our Talent Community View Profile Search by Keyword Select how often (in days) to receive an alert: Create Alert × Select how often (in days) to receive an alert: AI/ML Support Analyst Apply now » Date: Sep 7, 2026 Location: Saudi Arabia Company: King Abdullah University of Science & Technology About the Role The AI/ML Support Analyst will be a key member of the KAUST Supercomputing Lab’s (KSL) AI/ML Support Team, supporting the delivery of AI research services to KAUST's diverse research community. Working under the AI/ML Support Team Lead, this role focuses on developing and optimizing Generative AI models, maintaining computational benchmarks, and providing expert consultation to researchers across multiple scientific domains, including Climate & Weather, Bioinformatics, CFD, NLP, and multimodal AI. The analyst will help bridge the gap between cutting-edge computational infrastructure and the diverse needs of the research community, contributing to governance, technical enablement, and community development initiatives. Responsibilities Generative AI Development and Consulting Providing timely and useful user support via telephone, walk-in, email, and ticketing system submissions for all types of inquiries. Maintain high customer service standards in dealing with and responding to user issues and questions. Develop and consult on large-scale Generative AI model training on domain-specific datasets across research areas including Climate & Weather, Bioinformatics, Computational Fluid Dynamics (CFD), NLP, and multimodal AI. Support researchers in fine-tuning foundation models on domain-specific datasets using advanced optimization techniques. Develop data engineering pipelines to support AI research workflows. Design and implement efficient AI workflows optimized for KSL's high-performance computing environment. Build and maintain secure, OCI-compliant, HPC-ready container images using Singularity, Podman, or similar. Develop complex workflows using SLURM and Kubernetes for distributed training and inference. Governance and Compliance Support Conduct computational readiness reviews for AI research projects. Assist in AI model and artifact control reviews to ensure compliance with institutional standards. Support researchers in designing secure, compliant, and performant workflows. Provide expert consultation to researchers on efficient utilization of AI resources and best practices. Support the implementation of usage monitoring and reporting systems. Ensure user workflows comply with KSL security policies and best practices. Benchmarking and Quality Assurance Develop and maintain computational benchmarks for AI workloads on KSL systems. Create and maintain regression testing workloads to stress test system functionality. Support performance debugging and optimization activities for research workloads. Contribute to technology evaluation and benchmarking exercises for future infrastructure investments. Perform benchmarking of new hardware and software configurations. Training and Documentation Create comprehensive training materials for end-users on KSL’s HPC systems hosting AI workloads and tools. Develop and maintain high-quality technical documentation. Support the delivery of workshops on distributed training, fine-tuning, and inference optimization. Contribute to knowledge transfer initiatives within the KAUST research community. Provide one-on-one consultation to researchers on efficient use of computational resources. Qualifications Bachelor's or master’s degree in computer science, Data Science, Computational Science, Artificial Intelligence, or a related field. Strong academic foundation in machine learning, deep learning, and AI fundamentals. Required Skills Technical Skills - Essential Programming: Proficiency in Python; experience with R, Julia, Rust or C/C++ is a plus. AI/ML Frameworks: Strong expertise in PyTorch and/or TensorFlow, JAX or similar. Generative AI: Experience with foundation model development and fine-tuning techniques. HPC Systems: Experience developing complex workflows using SLURM and/or Kubernetes. Containerization: Experience building efficient HPC-ready container images using Singularity, Podman or similar. Data Engineering: Experience with data engineering techniques for developing AI pipelines. Linux: Strong Linux/Unix skills and bash

Bachelor's or master’s degree in computer science, Data Science, Computational Science, Artificial Intelligence, or a related field. Strong academic foundation in machine learning, deep learning, and AI fundamentals.

Generative AI Development and Consulting: Providing timely and useful user support via telephone, walk-in, email, and ticketing system submissions for all types of inquiries. Maintain high customer service standards in dealing with and responding to user issues and questions. Develop and consult on large-scale Generative AI model training on domain-specific datasets across research areas including Climate & Weather, Bioinformatics, Computational Fluid Dynamics (CFD), NLP, and multimodal AI. Support researchers in fine-tuning foundation models on domain-specific datasets using advanced optimization techniques. Develop data engineering pipelines to support AI research workflows. Design and implement efficient AI workflows optimized for KSL's high-performance computing environment. Build and maintain secure, OCI-compliant, HPC-ready container images using Singularity, Podman, or similar. Develop complex workflows using SLURM and Kubernetes for distributed training and inference. Governance and Compliance Support: Conduct computational readiness reviews for AI research projects. Assist in AI model and artifact control reviews to ensure compliance with institutional standards. Support researchers in designing secure, compliant, and performant workflows. Provide expert consultation to researchers on efficient utilization of AI resources and best practices. Support the implementation of usage monitoring and reporting systems. Ensure user workflows comply with KSL security policies and best practices. Benchmarking and Quality Assurance: Develop and maintain computational benchmarks for AI workloads on KSL systems. Create and maintain regression testing workloads to stress test system functionality. Support performance debugging and optimization activities for research workloads. Contribute to technology evaluation and benchmarking exercises for future infrastructure investments. Perform benchmarking of new hardware and software configurations. Training and Documentation: Create comprehensive training materials for end-users on KSL’s HPC systems hosting AI workloads and tools. Develop and maintain high-quality technical documentation. Support the delivery of workshops on distributed training, fine-tuning, and inference optimization. Contribute to knowledge transfer initiatives within the KAUST research community. Provide one-on-one consultation to researchers on efficient use of computational resources.