EntroMetrix
Machine Learning Engineer, Industrial Optimisation
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Machine Learning Engineer, Industrial Optimisation
About Us
Manufacturing is one of the biggest, hardest, and most important systems in the world, but many factories still make critical operational decisions with fragmented data and limited intelligence. EntroMetrix is changing that by building physics-informed AI that helps industrial teams run more efficient, resilient, and sustainable operations.
We are a small team founded by engineers from Cambridge and Imperial, working on a problem where better software can have a real-world impact on energy, materials, and production. If you want to build serious technology, work close to real industrial customers, and have a direct hand in shaping an early company, EntroMetrix is the place to do it. We are hiring across a number of roles. If you're a fit, apply.
Role: Machine Learning Engineer
We are looking for a Machine Learning Engineer to help build frontier models that 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 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.
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Requirements
- A degree in machine learning, computer science, engineering, physics, mathematics, applied mathematics, operations research, or a closely related STEM field from a world-leading university (PhD-level).
- Strong practical experience building machine learning models in Python, preferably 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.
- Willingness to work in person from our London office, typically 4 to 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.
- Experience with Bayesian approaches, multi-fidelity data streams, symbolic regression, glass-box modelling, or reinforcement learning.
- Publications or research experience in scientific ML, machine learning for physical systems, or applied optimisation.
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Why Join Us
- Competitive compensation package.
- Ownership of a critical technical layer at an early-stage company.
- The chance to build frontier AI models that help 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 operations to help decarbonise industry and improve operational resilience.
- A small, technical founding team with high ownership, honest feedback, and no fluff.
- Unlimited coffee.
How to Apply
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.
For questions or any adjustments to your application, email: info@entrometrix.ai. You can also follow us on LinkedIn for updates.
Note:
These are in-person roles based in London. We are currently unable to offer visa sponsorship; applicants must already have the right to work in the UK. We read every application, but the volume means we cannot always reply individually. If you have not heard back within two weeks, please assume we have not been able to take your application forward this time.
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