Description:
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.
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 world-leading university (PhD-level).
- 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.
- 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.
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 theatre.
- Unlimited coffee