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Machine Learning Engineer (Applied ML, B2B SaaS)

aspaara AG
Zürich, ZH

Direkt bewerben

Details zur Stelle

Qualifikationen

Doktorat
Englisch
Master
Docker
Python

Vollständige Stellenbeschreibung

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
**

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.

Direkt bewerben

Erweiterte Suche

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