Machine Learning Engineer – Signal Processing

Nanological; Valencia; Híbrido; EUR 40.000 - 60.000; Jornada completa

Descripción de la vacante

A leading company specialized in diagnostic technology is looking for a Machine Learning Engineer to work on a breakthrough platform. This role involves analyzing sensor data and developing ML models to enhance diagnostic accuracy, making a real clinical impact while contributing to a first-of-its-kind medical product.

Servicios

Competitive compensation

Opportunities for professional growth

Collaboration with leading research and clinical institutions

Formación

• 2–4 years applying ML and signal processing to real-world sensor data.
• Proven experience in ML pipeline development and product-aligned implementation.
• Experience working in cross-functional teams (hardware and software).

Responsabilidades

• Analyze and process optomechanical sensor signals to enhance signal-to-noise ratio.
• Develop and evaluate ML models for bacterial classification.
• Build modular, reusable code for signal processing and model pipelines.

Conocimientos

Python

Signal Processing

Machine Learning

Educación

Degree in Electrical Engineering, Physics, Mathematics, Computer Science or related field

Herramientas

NumPy

pandas

scikit-learn

PyTorch

TensorFlow

Docker

Descripción del empleo

Machine Learning Engineer – Signal Processing for Diagnostics Madrid (Parque Científico) | On-site with 1 remote day / week Deep Tech

• Sensors
• Diagnostics About the Company - Nanological is a CSIC spin-off developing a breakthrough diagnostic platform for the rapid and precise detection of sepsis. Our proprietary system integrates microfluidics, optomechanical sensing and machine learning to identify pathogens directly from blood within minutes. We've been recognized as a Deep Tech Pioneer by Hello Tomorrow, selected among the Top 100 startups by APTE and awarded by EIT Health, Comunidad de Madrid and the Spanish Ministry of Equality for our innovation and clinical impact. About the Role - We are looking for a Machine Learning Engineer with strong foundations in signal processing and a product-oriented mindset. You'll work with real sensor data to extract bacterial signatures and building a robust, scalable ML subsystem for integration into a regulated diagnostic product. This is a unique opportunity to shape a high-impact diagnostic product based on deep technology. Responsibilities
• Analyze and process optomechanical sensor signals to enhance signal-to-noise ratio and to extract diagnostic-relevant features
• Develop and evaluate ML models for bacterial classification
• Build modular, reusable code for signal processing and model pipelines
• Define and track sensitivity / specificity and other performance metrics
• Ensure compatibility with embedded and cloud-based integration
• Help lay the foundations of a production-ready ML subsystem Qualifications
• Degree in Electrical Engineering, Physics, Mathematics, Computer Science or a related field
• 2–4 years applying ML and signal processing to real-world sensor or experimental data
• Proven experience in ML pipeline development and product-aligned implementation
• Experience working in cross-functional teams (hardware and software) Technical Skills
• Strong Python (NumPy, pandas, scikit-learn, PyTorch / TensorFlow)
• Solid understanding of signal processing (filtering, spectral analysis, feature extraction)
• Experience with ML classification models and deep learning frameworks
• Familiarity with CI / CD, version control, modular design and Docker
• Bonus : experience in C++ or embedded system development Why Join Nanological
• Contribute to a first-of-its-kind medical product with real clinical impact
• Join a highly skilled, mission-driven team
• Gain technical ownership from day one
• Collaborate with leading research and clinical institutions
• Competitive compensation and real opportunities for professional growth Equal Opportunity - Nanological is an equal opportunity employer. We value diversity and are committed to creating an inclusive and respectful workplace for all. How to Apply - Send your CV and a short introduction to
• If you're ready to build something meaningful — we'd love to hear from you.

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