Machine Learning Engineer

Machine Learning Engineer | AI Start-Up | London | £100–120k + Equity

The Company

Our client is a well-funded AI start-up developing advanced machine learning technology to solve complex, real-world problems across the energy and natural resources sector.

Combining probabilistic machine learning, graph-based models and large-scale real-world datasets, their technology helps organisations make better decisions around major infrastructure projects, improving efficiency, reducing costs and supporting the energy transition.

With established commercial customers and backing from leading investors, the business is now scaling its core technology and engineering team.

They're looking for an experienced Machine Learning Engineer with strong Python, PyTorch and mathematical modelling skills to take ownership of improving and optimising the sophisticated ML models underpinning their technology.

Why This Role Stands Out

• Work with advanced probabilistic ML, graph-based models and PyTorch, tackling complex problems involving real-world data.
• Focus on model optimisation, accuracy and robustness, with genuine ownership of improving existing machine learning models.
• Work directly with the CTO and experienced ML researchers, influencing how models are developed and improved.
• Solve challenging engineering problems involving computational efficiency, statistical performance and production reliability.
• Join a well-funded AI start-up with established customers, meaningful equity and genuine commercial applications.

The Role

As a Machine Learning Engineer, you'll take ownership of improving the performance, accuracy and reliability of the machine learning models at the heart of the company's product.

The emphasis is on hands-on model development, optimisation and engineering, with dedicated ML researchers developing new modelling techniques and an MLOps Engineer managing infrastructure and deployment.

You'll work with sophisticated probabilistic and graph-based models, getting into the underlying mathematics and Python/PyTorch code to identify weaknesses, resolve performance bottlenecks and improve how models behave on complex real-world datasets.

This will involve everything from numerical optimisation and inference performance to model calibration, testing and production hardening.

Working closely with the CTO and research team, you'll help bridge the gap between advanced ML research and reliable commercial software, with your improvements directly influencing live customer projects.

What You'll Be Working On

• Machine Learning Model Optimisation: Improve the accuracy, statistical performance, robustness and computational efficiency of existing models.
• Python & PyTorch Engineering: Refactor, debug, test and optimise complex ML code, improving maintainability and production reliability.
• Probabilistic & Graph-Based ML: Work with sophisticated mathematical models, improving inference, uncertainty calibration and performance.
• Model Evaluation & Testing: Develop rigorous validation approaches, identify model weaknesses and measure the impact of improvements.
• Computational Performance: Optimise numerical operations, investigate bottlenecks and improve model efficiency across large, complex datasets.
• Research to Production: Collaborate with ML researchers to turn advanced modelling techniques into robust, scalable implementations.

Your Experience

We're looking for a technically strong, hands-on Machine Learning Engineer who enjoys understanding the mathematics behind ML models and improving how they perform.

You'll ideally have experience owning production machine learning models, with the ability to work beyond established frameworks and standard hyperparameter tuning to make meaningful improvements to the underlying models.

Essential Experience

• Commercial experience developing, improving and maintaining production machine learning models.
• Strong Python and PyTorch programming skills, with experience working directly with model implementations.
• Solid mathematical and statistical foundations, including model evaluation, optimisation and performance analysis.
• Demonstrable experience improving model accuracy, inference performance, computational efficiency or robustness .
• Experience debugging, refactoring and testing complex ML code in production environments.
• Ability to work independently on challenging modelling problems and collaborate effectively with research and engineering teams.

Nice to Have

• Graph Neural Networks (GNNs), graph-based learning or PyTorch Geometric.
• Probabilistic machine learning, Bayesian modelling or uncertainty quantification.
• Numerical optimisation, sparse linear algebra or computational performance profiling.
• Experience with physical-world data , including geospatial, environmental, scientific, sensor or engineering datasets.
• Experience translating research models into production-quality software.
• Familiarity with AWS, MLflow, Weights & Biases or similar ML tooling.
• Master's degree or PhD in Machine Learning, Statistics, Mathematics or a related discipline.

Experience in energy or natural resources is not essential. We're particularly interested in engineers who have worked on complex mathematical models involving real-world data and can demonstrate meaningful improvements to model performance.

Why Join?

• £100–120k salary plus meaningful equity in an early-stage, well-backed AI business.
• Work on sophisticated ML technology addressing major real-world challenges.
• Genuine ownership of model improvement, optimisation and production engineering.
• Collaborate with highly experienced ML researchers and engineers in a small, technically ambitious team.
• See your engineering work directly influence commercial products and customer outcomes.

If you're a Machine Learning Engineer who enjoys getting under the bonnet of complex models, understanding the maths behind them and making them faster, more accurate and more reliable, click apply to learn more.