Deep Learning Enginer

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Overview

Quantum Computing & AI software company based in Barcelona seeks a Deep Learning Engineer. This is a fixed-term contract until June 2026.

Responsibilities

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• Lead the end-to-end design, training, optimization, and evaluation of deep learning models (LLMs and computer vision), from data preparation through large-scale distributed training and deployment.
  
• Research, apply, and advance state-of-the-art model compression techniques—including pruning, distillation, quantization, and architectural optimisation—to balance accuracy, latency, cost, and hardware constraints.
  
• Build and maintain reproducible, automated pipelines for large-model training and compression, incorporating ablation studies, benchmarking, and systematic evaluation.
  
• Develop and curate datasets and fine-tuning strategies (e.g., SFT, preference optimisation, prompt engineering) tailored to domain-specific and real-world use cases.
  
• Integrate compressed models into production systems, collaborating closely with cross-functional teams while maintaining high engineering standards, documentation, and code quality.
  

Qualifications

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• Master’s or Ph.D. in Computer Science, Machine Learning, Electrical Engineering, Physics, or a closely related technical field.
  
• 3+ years of hands‑on experience training deep learning models from scratch, including architecture design, data pipelines, training loops, and distributed training.
  
• Strong expertise in model compression methods (pruning, distillation, low-rank factorisation, quantization) and performance analysis through ablations and error diagnostics.
  
• Deep understanding of modern model architectures (LLMs and/or computer vision), training dynamics, optimisation techniques, and the full model lifecycle. xghoner
  
• Proficiency with Python, PyTorch, and modern ML tooling, along with experience building scalable, reproducible training pipelines and optimizing models for real-world deployment constraints.
  

If this role is of interest please apply directly on LinkedIn or send a copy of your CV to .

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