Deep Learning Engineer

Deep Learning Engineer | Tech Start up (€200m+ raised) | Immediate Start | Zaragoza (Hybrid)A fantastic opportunity for a driven Engineer to join a leading Quantum AI company, who work on cutting-edge solutions that make AI faster, greener, and more accessible. You’ll be working alongside world-leading experts in quantum computing and AI, developing solutions that deliver real-world impact for global clients.Responsibilities Design, train, and optimize deep learning models from scratch (including LLMs and computer vision models), working end-to-end across data preparation, architecture design, training loops, distributed compute, and evaluation. Apply and further develop state-of-the-art model compression techniques, including pruning (structured/unstructured), distillation, low-rank decomposition, quantization (PTQ/QAT), and architecture-level slimming. Build reproducible pipelines for large-model compression, integrating training, re-training, search/ablation loops, and evaluation into automated workflows. Design and implement strategies for creating, sourcing, and augmenting datasets tailored for LLM pre-training and post-training, and computer vision models. Fine-tune and adapt language models using methods such as SFT, prompt engineering, and reinforcement or preference optimization, tailoring them to domain-specific tasks and real-world constraints.Required Qualifications Master’s or Ph.D. in Computer Science, Machine Learning, Electrical Engineering, Physics, or a related technical field. 3+ years of hands-on experience training deep learning models from scratch, including designing architectures, building data pipelines, implementing training loops, and running large-scale distributed training jobs. Proven experience in at least one major deep learning domain where training from scratch is standard practice, such as computer vision (CNNs, ViTs), speech recognition, recommender systems (DNNs, GNNs), or large language models (LLMs). Strong expertise with model compression techniques, including pruning (structured/unstructured), distillation, low-rank factorization, and architecture-level optimization. Demonstrated ability to analyze and improve model performance through ablation studies, error analysis, and architecture or data-driven iterative improvements. In-depth knowledge of foundational model architectures (computer vision and LLMs) and their lifecycle: training, fine-tuning, alignment, and evaluation. Solid understanding of training dynamics, optimization algorithms, initialization schemes, normalization layers, and regularization methods. Hands-on experience with Python, PyTorch and modern ML stacks (HuggingFace Transformers, Lightning, DeepSpeed, Accelerate, NeMo, or equivalent). Experience building robust, modular, scalable ML training pipelines, including experiment tracking, reproducibility, and version control best practices.And don’t worry if you’re not a perfect match — if you’re close and motivated to grow, you’re encouraged to apply.In accordance with local employment laws, applicants must have current, valid authorisation to work in Spain at the time of application. We are unable to sponsor employment visas for this role. Applications from individuals without existing work authorisation for Germany cannot be considered.If this sounds interesting and you'd like to learn more, click the link below to apply or email me with a copy of your CV at william@eu-recruit.comBy applying to this role, you understand that we may collect your personal data and store and process it on our systems. For more information, please see our Privacy Notice (