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).