Deep Learning Enginer

European Tech Recruit; Barcelona; Presencial; EUR 50.000 - 70.000; Jornada completa

Descripción de la vacante

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.

Formación

• 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.

Responsabilidades

• 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.

Conocimientos

Deep learning model training

Model compression techniques

Data preparation

Python

PyTorch

Educación

Master’s or Ph.D. in relevant field

Herramientas

Machine learning tooling

Descripción del empleo

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].