Machine Learning Engineer - Hybrid Modelling

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.You will be joining a highly talented team of scientists and engineers who are tackling a difficult technical challenge and applying new computational methods to real-world problems.The role offers autonomy, access to leadership, and the opportunity to help shape both technology strategy and future product direction.In this role you will work across the full lifecycle of scientific and engineering modelling, turning complex data into pratical insights Responsibilities include: 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.To be successful in this role you will need a strong quantitative background and a track record of applying advanced modelling techniques to real-world challenges.You will likely have: 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.What’s on Offer Opportunity to play a pivotal role in a rapidly growing deep-tech company.High-impact position with significant technical ownership.Competitive salary package.Meaningful equity participation.Clear progression path into technical leadership.Collaborative, highly capable and mission-driven team environment.The chance to work on genuinely novel technology with impact.Full details of the company and technology will be shared with shortlisted candidates.#J-*****-Ljbffr

To be successful in this role you will need a strong quantitative background and a track record of applying advanced modelling techniques to real-world challenges. You will likely have: 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.

What’s on Offer Opportunity to play a pivotal role in a rapidly growing deep-tech company. High-impact position with significant technical ownership. Competitive salary package. Meaningful equity participation. Clear progression path into technical leadership. Collaborative, highly capable and mission-driven team environment. The chance to work on genuinely novel technology with impact. Full details of the company and technology will be shared with shortlisted candidates. #J-*****-Ljbffr