Machine Learning Engineer (Applied ML, B2B SaaS), Zürich (Kreis 11)

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.

#J-18808-Ljbffr