We are seeking a highly motivated AI/ML Engineer to join our team in Spain. This is a permanent position offering the opportunity to work at the forefront of artificial intelligence and machine learning innovation.

The successful candidate will play a key role in the development, training, and optimization of models, with a focus on enhancing algorithmic performance and predictive accuracy for demanding engineering applications. You will be part of a multidisciplinary team developing intelligent systems and automated solutions from concept through to production. This role offers long-term career growth and the opportunity to shape the digital transformation and data-driven strategy of innovative platforms.

All applicants must be eligible to work in Spain.

Responsibilities / What you will do

• Lead and support the development of new machine learning models, with an emphasis on deep learning, neural networks, and advanced algorithms.

• Characterize and optimize model performance metrics (e.g., accuracy, precision, recall, latency, and scalability).

• Conduct experimental testing and analysis of large datasets, feature engineering, and model architectures.

• Collaborate with software engineers and product teams to ensure model performance aligns with end-use applications and production environments.

• Develop and maintain ML pipelines, data standards, and automated testing frameworks for model qualification.

• Support R&D projects, contributing to technical reports, publications, and presentations on AI advancements.

• Stay current with emerging AI research, frameworks, and industry standards in machine learning and data science.

Experience / Who you are

• Bachelor’s degree (or higher) in Computer Science, Data Science, Mathematics, or a related field.

• Proven experience in the development and deployment of machine learning models (e.g., NLP, computer vision, or predictive analytics).

• Strong background in statistical analysis and algorithmic characterization.

• Hands-on experience with ML testing techniques and frameworks (e.g., PyTorch, TensorFlow, Scikit-learn).

• Ability to design and execute data experiments, interpret complex datasets, and draw actionable conclusions.

• Proficiency in technical reporting and the ability to communicate AI results to both technical and non-technical stakeholders.

• Knowledge of MLOps practices, model versioning, and process–data interactions.

• Familiarity with cloud computing platforms (AWS, Azure, GCP) or simulation tools for testing model behavior.

• Experience with data quality systems and AI ethics/qualification frameworks (e.g., ISO standards or industry-specific regulations).