Machine Learning Engineer (Optimization & Prediction)

aspaara AG

New

Machine Learning Engineer (Optimization & Prediction)

Z�rich, Switzerland

Employee

Data Processing, Data Engineer

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 processes. In 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 You?ll 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

• Master?s 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 don?t just build models, you understand the business problem behind them
• Fuent in English
• Nice to Have
  
  

**

Required

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, we?d love to hear from you.

Updated: 13 hours ago

Job ID: 16004769

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aspaara AG

11-50 employees

Technology, Information and Internet

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 op?

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Matthew Gilberthorpe

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