BCAST Industrial Services
Machine Learning Engineer
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Company Description
BCAST Industrial Services is the commercial and technical services arm of the Brunel Centre for Advanced Solidification Technology at Brunel University London, a globally recognized leader in solidification science and light metals. The organization partners with aerospace, automotive, and manufacturing companies that require deep materials expertise and support beyond standard testing services. Its work focuses on solidification science, liquid metal engineering, and metallic alloy recycling, backed by EPSRC and EU funding and long-term collaborations with industrial partners such as Constellium and JLR.
BCAST Industrial Services offers project-specific and flexible engagement models, supported by a 4,000m² campus that includes the Advanced Metals Casting Centre, Advanced Metal Processing Centre, and the Future Metallurgy Centre. The team also delivers training courses in metallurgy and materials science, providing a rich environment for technical growth.
Role Description
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This is a full-time hybrid role based in Greater London, with a mix of on-site work at Brunel University London and the option to work from home for part of the schedule. The Machine Learning Engineer will design, develop, and deploy machine learning models to support materials research, process optimization, and industrial decision-making in areas such as solidification, casting, and alloy development.
Day-to-day responsibilities include:
- Collecting and preparing data from experimental facilities
- Building and evaluating predictive models
- Collaborating with materials scientists and engineers to translate research questions into ML solutions
- Implementing algorithms for pattern recognition and neural networks
- Performing statistical analysis
- Contributing to software tools that integrate ML into engineering workflows
The Machine Learning Engineer will also:
- Document methodologies
- Present findings to stakeholders
- Help improve the scalability and robustness of data-driven solutions across industrial projects
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Qualifications
- Strong foundation in Computer Science and Algorithms, with the ability to design efficient, maintainable, and scalable ML solutions.
- Proficiency in Statistics and Pattern Recognition for data analysis, model evaluation, and interpretation of experimental results.
- Hands-on experience with Neural Networks and modern machine learning frameworks (e.g., TensorFlow, PyTorch, scikit-learn).
- Ability to work with scientific and engineering datasets, including data cleaning, feature engineering, and model deployment in production-like environments.
- Bachelor’s or Master’s degree in Computer Science, Data Science, Engineering, Applied Mathematics, or a related field; a PhD or experience in materials science or manufacturing is an advantage.
- Competence in programming languages commonly used in ML (such as Python, R, or C++), and familiarity with version control and collaborative development tools.
- Strong analytical
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