Machine Learning Scientist
Role details
Job location
Tech stack
Job description
This role sits at the intersection of machine learning, simulation, and vehicle dynamics, applying modern ML techniques to complex engineering problems. You will work with large-scale simulation data and engineering datasets to develop models that accelerate simulations, improve performance prediction, and support engineering decision-making. You will have the opportunity to
The role sits within the Performance area and involves close collaboration with engineers across simulation, vehicle dynamics, and design. This position reports to the Head of Performance Software Applications.
We are a small, highly collaborative team that values curiosity, technical depth, and ownership. We're looking for someone comfortable moving between research and engineering, who enjoys taking ideas from early experimentation through to production systems used by engineers and trackside teams., In this role you will:
- Research, design and develop machine learning models and methodologies for simulation acceleration, surrogate modelling, and performance prediction.
- Own ML solutions end-to-end, from problem definition and experimentation through training, evaluation, deployment, and integration into engineering workflows.
- Work with large-scale simulation outputs and engineering datasets, transforming them into reliable models used in performance-critical workflows.
- Improve ML infrastructure by strengthening data pipelines, testing frameworks, and deployment processes.
- Collaborate with engineers and domain experts to integrate ML models into real engineering workflows and production environments, including trackside use cases.
Requirements
Do you have a Master's degree?, * MSc or PhD in AI, Computer Science, Engineering, Mathematics, Physics, or a related field.
- Strong Python skills.
- Proven industry experience with at least one ML framework such as PyTorch, TensorFlow, or JAX
- Strong foundations in machine learning and deep learning, including:
- Linear algebra, statistics, optimisation.
- Practical experience in: data preparation, model architectures, hyperparameter tuning, evaluation techniques, and model validation.
- Practical experience with software development best practices (code quality, reviews, testing, maintainability, and collaboration)., * Proven industry experience deploying machine learning models for inference or production environments.
- Familiarity with containerisation technologies (Docker, Kubernetes).
- Experience with Git workflows and CI/CD pipelines.
- Engineering background or experience working with physical systems., You are someone with strong machine learning fundamentals who enjoys turning ideas into working systems that deliver real impact.
You are comfortable working across the stack - from research and model development to the engineering required to deploy reliable ML solutions. You thrive in small, fast-moving teams, take ownership of problems, and enjoy collaborating closely with domain experts to solve challenging technical problems.
Benefits & conditions
Our riverside campus is powered by 100% renewably sourced energy and features an on-site gym and exercise studio, subsidised restaurant and on-site parking with EV chargers available.
We offer a competitive and attractive package of benefits including a generous bonus scheme, Mercedes car lease scheme, private medical cover, life assurance and 25 days holiday. We pride ourselves on our family-friendly environment and employee well-being programmes.
Why Us:
We believe that building a more inclusive and diverse culture helps us go faster and further.
From recruitment and building our future talent pipeline to internal communications and leadership training, we're building a team where everyone can thrive and contribute to our shared success.
Our aim is to attract, develop and retain exceptional people from all backgrounds, creating a workplace where all team members feel respected, supported and able to fulfil their potential.
