AI/ML Engineer

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).