AI Engineer (Applied AI, B2B SaaS)

ppbAbout the Company /b /p pWe are a bfast-growing B2B SaaS company /b building an bAI-driven platform /b to transform automotive aftersales operations. /p pOur product helps bodyshops, dealer groups, and multi-site operators boptimize workflows, improve efficiency, and make better decisions through data. /b 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 bplan work more accurately, assign jobs to the right people, and predict when work will be completed. /b At the core of our platform is our AI: bmachine learning models and optimization algorithms /b that power intelligent predictions and decisions in complex operational environments. /p pWith customers across multiple European markets and strong early traction, we are now looking to hire an bAI Engineer (Artificial Intelligence) /b to further strengthen our product and AI capabilities. /p pDo 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. /p h3Tasks /h3 ul liDesign and deploy bAI models for prediction and decision-making /b on domain-specific operational data /li liAdvance and extend bscheduling and resource optimisation /b, including multi-objective optimisation, constraint handling, and stable re-planning /li liBuild bend-to-end models /b that learn from real operational data and improve planning accuracy over time /li liOwn the bfull AI lifecycle /b: data analysis, feature engineering, model development, evaluation, and monitoringTranslate bbusiness problems into formal models and measurable outcomes /b /li /ul pbExamples of What You'll Build /b /p ul liCapacity planning algorithms that account for skills, time buffers, cool-down periods, and space constraints /li liIntelligent job-to-talent matching with dynamic weighting for in-progress vs. new

projects /li liAutomated project creation from structured and unstructured operational inputs (e.g. PDFs, free text) /li /ul h3Requirements /h3 ul liMaster's or PhD in Computer Science, Mathematics, Physics or a related field /li liSeveral years of experience in machine learning, particularly with tabular data and time series /li liStrong knowledge of at least one deep learning framework (PyTorch is preferred) /li liExperience designing optimisation algorithms and heuristics for complex, constrained problems /li liProficient in Python and the data science ecosystem (e.g. pandas, scikit-learn) /li liExperience with ML experiment tracking and model deployment (e.g.

MLflow, Docker, Kubernetes) /li liA self-driven, solution-oriented mindset: you don’t just build models, you understand the business problem behind them /li liFluent in English /li /ul pbNice to Have /b /p pBeyond our core artificial intelligence, we are exploring additional AI capabilities that will flow into the product over time. Experience in this area is not required, but a plus: /p ul liExperience with LLMs or agentic systems (e.g.

RAG, information extraction from unstructured data, API-based workflows) /li /ul h3Benefits /h3 ul liCreative freedom in a young, technically ambitious team /li liDirect impact of your work on the product and our customers /li liA modern tech stack and a culture that encourages experimentation /li liA real-world, domain-rich problem space, not another generic SaaS /li liEnd-to-end ownership of AI features, from data exploration to production deployment /li /ul pIf you want to work on meaningful AI problems, take ownership, and see your work used in the real world, we'd love to hear from you. /p /p