Deep Learning Enginer

European Tech Recruit; Barcelona; On-site; EUR 50,000 - 70,000; Full time

Job summary

A technology recruitment firm in Barcelona seeks a Deep Learning Engineer with a Master's or Ph.D. in a relevant field. The role involves leading the design, training, and optimization of deep learning models while applying state-of-the-art model compression techniques. Candidates should have over 3 years of hands-on experience in deep learning, a profound understanding of model architectures, and proficiency in Python and PyTorch. This position offers a fixed-term contract until June 2026.

Qualifications

• 3+ years of experience training deep learning models from scratch.
• Strong expertise in model compression methods and performance analysis.
• Deep understanding of modern model architectures and training dynamics.

Responsibilities

• Lead design, training, optimization, and evaluation of deep learning models.
• Research and apply model compression techniques.
• Build and maintain automated pipelines for large-model training.

Skills

Deep learning model training

Model compression techniques

Data preparation

Python

PyTorch

Education

Master’s or Ph.D. in relevant field

Tools

Machine learning tooling

Job description

Overview

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

Responsibilities

• 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

• 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.
• 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 [e-mail].