Machine Learning Engineer - (LLM/Large Language Models) - Zaragoza
A fantastic opportunity for Jr/Mid level Machine Learning Engineer to join fast�growing deep�tech company, who provide hyper�efficient software to integral companies across finance, energy, manufacturing and cybersecurity to gain an edge with quantum computing and artificial intelligence.
This is initially a Fixed Term Contract until June 30th 2026 - Hybrid in Zaragoza
Responsibilities
- Design and develop new techniques to compress Large Language Models based on quantum�inspired technologies to solve challenging use cases in various domains.
- Conduct rigorous evaluations and benchmarks of model performance, identifying areas for improvement, and fine�tuning and optimising LLMs for enhanced accuracy, robustness, and efficiency.
- Use your expertise to assess the strengths and weaknesses of models, propose enhancements, and develop novel solutions to improve performance and efficiency.
- Act as a domain expert in the field of LLMs,



understanding domain�specific problems and identifying opportunities for quantum AI�driven innovation.
- Maintain comprehensive documentation of LLM development processes, experiments, and results.
- Participate in code reviews and provide constructive feedback to team members.

Required Qualifications
- Bachelors/Master's or Ph.D. in Artificial Intelligence, Computer Science, Data Science, or related fields.
- 2+ years of hands�on experience with deep learning models and neural networks, preferably working with Large Language Models and Transformer architectures, or computer vision models.
- 1+ year of hands�on experience using LLM and Transformer models, with excellent command of libraries such as HuggingFace Transformers, Accelerate, Datasets, etc.
- Excellent problem�solving, debugging, performance analysis, test design, and documentation skills.
- Excellent programming skills in Python and experience with relevant libraries (PyTorch, HuggingFace, etc.).
- Experience wit

📌 Machine Learning Engineer (Madrid)
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📍 Madrid