GRNET S.A. provides Internet connectivity, high-quality e-Infrastructures and advanced services to the Greek Educational, Academic and Research community, aiming at minimizing the digital divide and at ensuring equal participation of its members in the global Society of Knowledge. GRNET provides advanced services to the following sectors: Education, Research, Health, Culture.
In 2026, GRNET is expected to host the DAEDALUS supercomputer, which is expected to rank among the Europe’s top supercomputers and will also serve the Greek AI factory - Pharos with special needs for AI workflows. DAEDALUS is based on HPE’s NVIDIA GH200 direct liquid-cooled architecture, designed for about 89 petaflops sustained (115 petaflops peak) for traditional HPC, AI and Big Data/HPDA workloads across CPU and GPU-accelerated partitions backed by 1 PB of high-performance NVMe and 10 PB of usable storage.
As a Distributed AI Support Engineer, you will help researchers, startups, and industry teams turn this cutting-edge infrastructure into real-world AI breakthroughs, working alongside leading European universities, supercomputing centres, and industrial partners in the broader EuroHPC ecosystem. More specifically, you will contribute to the following focus areas. You are not expected to know all the technologies listed below. We are looking for strong AI and Python programming skills, solid fundamentals, and motivation to learn the necessary tools and workflows.
Provide first-line support for AI on HPC workloads (LLM, computer vision and other GPU-accelerated workloads): ticket triage, quick diagnosis of failed runs, escalation when hardware issues are suspected. Support users in writing/reviewing/debugging Slurm job scripts launching multi-GPU/multi-node jobs via torchrun, accelerate launch or deepspeed, and support Ray/DeepSpeed and vLLM inference workflows where appropriate.
Maintain and test shared AI/LLM and computer-vision stacks for HPC and Cloud (PyTorch, DDP/FSDP, Hugging Face Transformers & Accelerate, PEFT/LoRA, Unsloth, DeepSpeed, Bitsandbytes, TensorFlow, RAPIDS, Ray, vLLM and related tooling), ensuring compatibility with NVIDIA drivers, CUDA and NCCL. Design, publish and support recommended Apptainer/Singularity containers (including NGCbased images) for training, fine-tuning, inference and RAG.
Diagnose common AI/LLM failures (CUDA errors, NCCL timeouts, GPU OOM, distributed hangs, misconfigured environment). Validate driver/CUDA/NCCL stacks and profile/tune workloads using PyTorch Profiler, NVIDIA Nsight (Systems/Compute), TensorBoard, MLflow and Weights & Biases (WandB).
Guide users on scalable distributed training with PyTorch DDP/FSDP and DeepSpeed (ZeRO/pipeline/tensor parallelism), plus Ray and higher-level frameworks (PyTorch Lightning, Hydra), mapped to node/GPU topology. Support 8-bit/4-bit quantisation and QLoRA workflows (Unsloth, Bitsandbytes) and large-scale inference frameworks (vLLM, NVIDIA TensorRT-LLM, Triton Inference Server); contribute to AI/LLM and computer-vision benchmarking.
Advise on effective storage use for tokenised datasets, vector indices, checkpoints and logs (layout, sharding, cleanup). Troubleshoot dataloader/I/O bottlenecks and recommend suitable formats and caching/staging, including use of NVIDIA DALI, WebDataset, RAPIDS and Dask where appropriate.
Monitor AI/LLM usage metrics (GPU hours, job success rates, queue waiting times, typical model sizes/frameworks) to drive improvements in stacks, docs and training. Support Access Call evaluation via technical review of AI/LLM proposals and resource feasibility checks.
Develop and maintain task-oriented documentation and cookbooks for AI/LLM workflows on HPC and Cloud. Prepare hands-on tutorials/demos (PyTorch, TensorFlow, Hugging Face Transformers, vLLM, Ray/DeepSpeed, RAPIDS, JupyterLab/ TensorBoard/ MLflow).
Prepare technical reports on trainings offered; maintain dashboards/databases for trainings, KPIs and survey data. Prepare web content (news, training/service pages), coordinate announcements (newsletters, social media), and support stakeholders and user access processes.
• Frameworks and libraries: PyTorch, DDP, FSDP, Hugging Face Transformers, Accelerate, PEFT/LoRA, Unsloth, DeepSpeed (ZeRO, pipeline, tensor parallelism), Bitsandbytes, QLoRA, torchvision and other common computervision libraries; TensorFlow; vLLM; Ray Train; Hugging Face Datasets, SentencePiece, FAISS (faissgpu), Gradio, and supporting Python libraries such as SciPy, Matplotlib and Optimum.
• Launchers and schedulers: torchrun, accelerate launch, deepspeed, Slurm or similar HPC schedulers, including typical srun / salloc multi-node launch patterns and Ray-based multi-node launchers.
•Profiling and debugging: PyTorch Profiler, NVIDIA Nsight Systems/Compute, CUDA tools, NCCL debugging, TensorBoard, MLflow, Weights & Biases (WandB), and HPC debuggers and profilers.
• Containers: Apptainer, (Singularity) for image creation and migration and Apptainer-based container workflows.
Requirements
• Degree in Computer Science, Engineering or a related STEM field. Applications from graduating students and recent graduates will be considered.
• Strong programming skills in Python and experience with AI frameworks and libraries (e.g. PyTorch, TensorFlow, Hugging Face Transformers, vLLM, Ray, etc.).
• Hands-on experience training or fine-tuning models on GPUs using PyTorch and related tooling (e.g. torchrun, DDP).
• Ability to communicate technical concepts clearly to researchers and industry users, both in writing (documentation) and in person (training, support).
• Familiarity with GPU architectures and concepts relevant to AI on HPC.
• Experience with LLM or foundation model training/fine-tuning, distributed training frameworks (FSDP, DeepSpeed) and quantisation methods (8-bit/4-bit, QLoRA, PEFT/LoRA, Bitsandbytes, Unsloth).
• Experience with profiling and monitoring tools (PyTorch Profiler, NVIDIA Nsight Systems/Compute, cluster monitoring stacks).
• Experience building or maintaining containerised environments for GPU workloads (Apptainer/Singularity) in an HPC context.
• Prior involvement in user support for HPC or research computing centres, including documentation, training and best-practice guides.
Benefits
GRNET provides a creative, dynamic and challenging working environment, that encourages team spirit, cooperation and continuous learning of state-of-the-art technology.
• Opportunities for International collaborations
• Competitive remuneration package
• Opportunities for professional development
• Modern, friendly and innovative working environment
GRNET is an equal opportunity employer that is committed to diversity and inclusion in the workplace. People with a diverse range of backgrounds are encouraged to apply. We do not discriminate against any person based upon their race, age, color, gender identity and expression, disability, national origin, medical conditions, religion, parental status, or any other characteristics protected by law.
All applications will be treated with strict confidentiality.
GRNET – National Infrastructures for Research and Technology, provides networking and cloud computing services to academic and research institutions, to educational bodies at all levels, and to agencies of the public, broader public and private sector. It is responsible for promoting and disseminating network and computing technologies and applications, as well as for promoting and implementing Greece’s Digital Transformation goals. Thus, GRNET leverages the educational and research activity in the country, towards the development of applied and technological research in the fields of telecommunication networks and computing services.GRNET holds a key role as the coordinator of all e-infrastructures in Education and Research. With twenty-plus years’ experience in the fields of advanced network, cloud computing and IT infrastructures and services, and significant international presence, GRNET shall advise the Ministry of Digital Governance on issues relating to the design of advanced information systems and infrastructures.GRNET develops synergies with other agencies which provide digital services in the Greek public sector, by sharing best practices and know-how on advanced information systems and it represents the national research and technological community within the European Union’s Research Infrastructures. GRNET contributes to the country’s Digital Transformation via in-depth analysis, technological studies, standard solutions and specialized know-how, serving at the same time hundreds of thousands of users on a daily basis in the strategic fields of Research, Education, Health and Culture.GRNET is also the National Research and Education Network (NREN).In order to reach its goals, GRNET undertakes projects, initiatives and other activities related to information technology, digital technology, communication, e-governance, new and open technologies, including new big data technologies, artificial intelligence and machine learning, and in general, to the promotion, dissemination and transfer of know-how regarding network and computing technologies and their applications, to research and development, education and to the promotion of Digital Transformation.
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