Machine Learning Engineer (Applied ML, B2B SaaS)

aspaara AG; Zürich; Vor Ort; Vertraulich; Vollzeit

Benefits dieser Stelle

Creative freedom

Direct impact

Modern tech stack

Experimentation culture

End-to-end ownership

Zusammenfassung

aspaara AG in Zürich is building an AI-driven platform to transform automotive aftersales operations. We are hiring a Machine Learning Engineer to design and deploy models, optimize scheduling and resource use, and push end-to-end ML capabilities in production.

You will work with real-world data, own the ML lifecycle, and translate business problems into measurable outcomes. Join a fast-growing B2B SaaS company shaping the future of workshops across Europe.

Qualifikationen

• Master’s or PhD in Computer Science, Mathematics, Physics or a related field.

Aufgaben

• Design and deploy machine learning models for prediction and decision-making on domain-specific operational data.

Kenntnisse

Machine learning

PyTorch

Python

Time series

Tabular data

Pandas

scikit-learn

MLflow

Docker

Kubernetes

OR-Tools

CP-SAT

Gurobi

English

Ausbildung

Master's or PhD in CS/Math/Physics

Tools

Docker

Kubernetes

MLflow

OR-Tools

Gurobi

Jobbeschreibung

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.