Machine Learning Engineer (Zaragoza y Barcelona) (temporal)
4 days ago
Valencia
We are a European deep-tech leader in quantum and AI, backed by major global strategic investors and strong EU support. Our groundbreaking technology is already transforming how AI is deployed worldwide — compressing large language models by up to 95% without losing accuracy and cutting inference costs by 50–80%. Joining us means working on cutting-edge solutions that make AI faster, greener, and more accessible — and being part of a company often described as a “quantum-AI unicorn in the making.” We offer • Competitive annual salary., • Two unique bonuses: signing bonus at incorporation and retention bonus at contract completion., • Relocation package (if applicable)., • Fixed-term contract ending in June 2026., • Hybrid role and flexible working hours., • Be part of a fast-scaling Series B company at the forefront of deep tech., • Equal pay guaranteed., • International exposure in a multicultural, cutting-edge environment. As a Machine Learning Engineer, you will • 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., • Build LLM based applications such as RAG and AI agents., • 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., • Design, train and deliver custom deep learning models for our clients, • Work in diverse areas beyond LLM, e.g., computer vision., • Maintain comprehensive documentation of LLM development processes, experiments, and results., • Share your knowledge and expertise with the team to foster a culture of continuous learning, guiding junior members of the team in their technical growth and helping them develop their skills in LLM development., • Participate in code reviews and provide constructive feedback to team members., • Stay up to date with the latest advancements and emerging trends in LLMs and recommend new tools and technologies as appropriate. Required Qualifications • Bachelor's, Master's or Ph.D. in Artificial Intelligence, Computer Science, Data Science, or related fields., • 2+ years of hands-on experience with designing, training or fine-tuning deep learning models, preferably working with transformer or computer vision models., • 2+ year of hands-on experience using transformer models, with excellent command of libraries such as HuggingFace Transformers, Accelerate, Datasets, etc.", • Solid mathematical foundations and theoretical understanding of deep learning algorithms and neural networks, both training and inference., • Excellent problem-solving, debugging, performance analysis, test design, and documentation skills., • Strong understanding with the fundamentals of GPU architectures and and LLM hardware/ software infrastructures., • Excellent programming skills in Python and experience with relevant libraries (PyTorch, HuggingFace, etc.)., • Experience with cloud platforms (ideally AWS), containerization technologies (Docker) and with deploying AI solutions in a cloud environment, • Excellent written and verbal communication skills, with the ability to work collaboratively in a fast-paced team environment and communicate complex ideas effectively., • Previous research publications in deep learning or any tech field is a plus, • Fluent in English Preferred Qualifications • Experience running large-scale workloads in high-performance computing (HPC) clusters., • Experience in handling large datasets and ensuring data quality., • Experience with inference and deployment environments (TensorRT, vLLM, etc.)., • Experience in accuracy evaluation of LLMs (OpenLLM Leaderboard)., • Experience building and evaluating RAG systems., • Experience in building non-LLM deep learning applications, e.g., computer vision, audio or signal processing., • Familiarity with AI ethics and responsible AI practices., • Experience in DevOps/MLOps practices in deep learning product development.
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