Machine Learning Engineer - Hybrid Modelling
Newton Colmore is partnered with a venture-backed deep tech company developing novel intelligent systems that will transform how complex industrial and scientific processes are designed, monitored, and optimised.
As the organisation continues to scale, they are seeking a machine learning engineer to take ownership of advanced modelling, machine learning, and predictive analytics initiatives at the heart of their technology platform.
In this role you will work across the full lifecycle of scientific and engineering modelling, turning complex data into pratical insights
Developing advanced mathematical and machine learning models for complex real-world systems.
Building predictive models and digital representations of physical processes.
Combining first-principles approaches with modern AI and machine learning techniques.




Designing and analysing experimental programmes to generate meaningful insights and improve model performance.
Working with large-scale sensor and operational datasets to develop forecasting and monitoring capabilities.
MSc or PhD in Mathematics, Physics, Statistics, Applied Mathematics, Computer Science, Engineering, or a related quantitative discipline.
Industrial experience developing computational, statistical or machine learning models.
Strong understanding of mathematical modelling, optimisation, simulation, or predictive analytics.
Excellent Python programming skills and experience working with modern scientific computing frameworks.
Any additional experience with digital twin technologies, Bayesian modelling, or working knowledge gained within an engineering or life sciences setting would be highly advantageous.

📌 Machine Learning Engineer - Hybrid Modelling (San Sebastián)
🏢 Newton Colmore
📍 San Sebastián