Applied AI / Machine Learning Engineer

EntroMetrix is an early-stage UK startup building physics-informed AI for industrial operations. We help manufacturers improve efficiency, sustainability and operational performance by turning data into actionable intelligence. Our models have already been validated on real industrial data, showing significant improvement potential, and we are now expanding deployment.

We are looking for a Machine Learning Engineer to help build frontier models to understand and improve complex operational systems. The work sits at the intersection of scientific machine learning, time-series modelling, optimisation and real-world deployment. You will work closely with the founding team, customer sites and industrial data to turn early technical validation into a scalable product. This is a hands-on engineering role; you will not just train models in isolation. You will build systems that need to work with messy data, operational constraints and real-world environments.

What you will do:

• Design, train and deploy machine learning models for complex operational systems.
• Work with sparse, noisy and irregular time-series data from real-world environments.
• Build models that combine data-driven learning with physical and operational constraints.
• Develop reusable modelling components that can scale across different sites and use cases.
• Work with the product and engineering team to move models from prototype to production.
• Evaluate model performance, reliability and robustness in applied settings.
• Spend time with customers to understand the operational context behind the data.
• Contribute to the technical direction of the platform as one of the first ML hires.

What we are looking for:

• A degree in machine learning, computer science, engineering, physics, mathematics, applied mathematics, operations research or a closely related STEM field from a top university.
• Strong practical experience building machine learning models in Python, ideally using PyTorch, JAX or similar frameworks.
• Experience with one or more of: scientific machine learning, physics-informed ML, time-series modelling, optimisation, simulation, forecasting or probabilistic modelling.
• Comfort working with messy real-world data, including missing values, drift, noise and inconsistent data quality.
• Interest in applying machine learning to physical systems, industrial operations and real-world optimisation problems.
• In-person working from our London office, typically 4–5 days per week, with occasional travel to customer sites in the UK.

Nice to have:

• Experience deploying ML models into production.
• Experience with optimisation, simulation, control systems or operations research.
• Exposure to industrial or operational data environments.
• Publications or research experience in scientific ML, machine learning for physical systems or applied optimisation.

Why join:

• Competitive compensation package.
• Ownership of a critical technical layer at an early-stage company.
• The chance to build frontier AI models that will define how factories are run over the next decade.
• Work directly with manufacturers across sectors, from large enterprises to SMEs, and see your models deployed in real industrial operations help decarbonise and improve resilience across manufacturing.
• A small, technical founding team with high ownership, honest feedback and no theatre.
• Unlimited coffee (other drinks also possible).

How to apply:

Apply on LinkedIn and send your CV, a short note on a technical project you are proud of, and a few lines on why you are interested in applying machine learning to real-world systems. We are hiring now, with interviews in May. For questions or adjustments for the role, please email the above, and follow our page for updates!

Please note: This is an in-person role based in London. We are currently unable to offer visa sponsorship, so applicants must already have the right to work in the UK.