Machine Learning Engineer

hace 5 días

Marzan, España Coleo A tiempo completo

Wastex is the technology company within the Coleo Group, a growing organization whose main activities include: # Design, development, and manufacturing of garment and fabric collections for major brands and retail chains, primarily using textile waste. # Management and revalorization of textile waste in waste treatment plants. # Development of technologies for classification, valorization, and traceability of textile waste and materials. The main mission of this role is to train, optimize, and improve real-time Machine Learning models used in our industrial machinery for textile recycling. Most ongoing projects are related to Computer Vision , combining open-source models with proprietary ones, especially when working with data from advanced sensors such as hyperspectral cameras . We are looking for someone proactive, innovative, and eager to experiment , who can identify the data they need and request it, plan and coordinate tests on-site with the QA team to validate improvements in new models. Looking ahead to next year, the Machine Learning area will evolve toward new strategic use cases for the business, progressively incorporating architectures such as Retrieval-Augmented Generation (RAG) . You will have the opportunity to participate from the early stages in high-impact initiatives alongside the AI team. Degree in AI, Data Science, or a related discipline. Fluent English. Strong skills in Python and PyTorch , with experience implementing and training models. Experience in Computer Vision and/or real-time ML projects . Availability to travel as required by the project. Knowledge of MLOps , dataset versioning tools (DVC, W&B, MLflow), and cloud environments such as AWS. Experience integrating models into industrial systems or real-world environments . Making a difference in transforming the textile industry. Growing a network of state-of-the-art recycling plants. Deeply committed to offering employment opportunities to people with disabilities and in risk of social exclusion. Flexible working hours and work from home arrangements.

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