Machine Learning Engineer (Optimization & Prediction)

About the Company

We are a fast-growing B2B SaaS company building an AI-driven platform to transform automotive aftersales operations.

Our product helps bodyshops dealer groups and multi-site operators optimize workflows improve efficiency and make better decisions through data. Using digital twin technology and real-time optimization we uncover operational improvements that are often hidden in day-to-day practice this means helping workshops plan work more accurately assign jobs to the right people and predict when work will be completed. At the core of our platform is our AI: machine learning models and optimization algorithms that power intelligent predictions and decisions in complex operational environments.

With customers across multiple European markets and strong early traction we are now looking to hire a Machine Learning Engineer to further strengthen our product and AI capabilities.

Do you want to help shape our award-winning artificial intelligence of tomorrow which is already improving the working lives of thousands of people across eight European countries today Become an integral part of our established yet dynamic startup team.

Tasks

• Design and deploy machine learning models for prediction and decision-making on domain-specific operational data
• Advance and extend scheduling and resource optimisation including multi-objective optimisation constraint handling and stable re-planning
• Build end-to-end models that learn from real operational data and improve planning accuracy over time
• Own the full ML lifecycle: data analysis feature engineering model development evaluation and monitoring
• Translate business problems into formal models and measurable outcomes

Examples of What Youll Build

• Capacity planning algorithms that account for skills time buffers cool-down periods and space constraints
• Intelligent job-to-talent matching with dynamic weighting for in-progress vs. new projects
• Automated project creation from structured and unstructured operational inputs (e.g. PDFs free text)

Requirements

• Masters or PhD in Computer Science Mathematics Physics or a related field
• Several years of experience in machine learning particularly with tabular data and time series
• Strong knowledge of at least one deep learning framework (PyTorch is preferred)
• Experience with mathematical optimisation and/or constraint programming (e.g. OR-Tools CP-SAT Gurobi)
• Proficient in Python and the data science ecosystem (e.g. pandas scikit-learn)
• Experience with ML experiment tracking and model deployment (e.g. MLflow Docker Kubernetes)
• A self-driven solution-oriented mindset: you dont just build models you understand the business problem behind them
• Fuent in English

**Nice to Have
**

Beyond our core AI we are exploring additional AI capabilities that will flow into the product over time. Experience in this area is not required but a plus:

• Experience with LLMs or agent-based systems (e.g. RAG information extraction from unstructured data API-based workflows)

Benefits

• Creative freedom in a young technically ambitious team
• Direct impact of your work on the product and our customers
• A modern tech stack and a culture that encourages experimentation
• A real-world domain-rich problem space not another generic SaaS
• End-to-end ownership of AI features from data exploration to production deployment

If you want to work on meaningful machine learning problems take ownership and see your work used in the real world wed love to hear from you.