ML Intern: Optimization Quality & Monitoring

About this role

Our common vision Our mission is to help marketing teams unlock their full growth potential with AI. Our AI-powered attribution creates real transparency, showing how digital advertising investments perform across channels. On that foundation, we optimize budgets so our customers can sustainably grow their bottom line. We support leading European brands across e-commerce, telco, banking, insurance & energy, and many other industries in making their marketing smarter, more efficient, and more impactful.

About the role Nexoya runs fully automated, AI-driven budget optimizations and simulations for enterprise marketing portfolios across many ad channels. Every result that reaches a customer must be correct, explainable and on time. As our ML Intern, you make sure it is: you monitor pipeline health, check the quality of predictions, optimizations and simulations, investigate anomalies and turn recurring manual checks into reliable tooling.

You work closely with our ML engineers and Customer Success, and you see directly how your work shapes our customers' budget decisions. Your responsibilities Monitor the automation and assure result quality Monitor the automated prediction, optimization and simulation pipelines across customer portfolios and track run health, completeness and timeliness. Verify that predictions, optimization proposals and simulation results meet our quality standards before they reach customers: budget shifts, expected KPI changes, channel constraints and funnel-level effects.

Investigate failed or unexpected runs, identify the root cause (data, configuration or model) and resolve it or escalate it with a clear diagnosis. Assure the quality of results over time Define and maintain plausibility checks for optimization and simulation results, for example for budget planning, seasonal shifts or new channel tests. Make sure results are consistent and explainable, so that customers and account managers can trust them without manual checks.

Compare optimized and simulated outcomes with realized performance, document deviations and feed the findings back to the ML team. Safeguard data and model quality Monitor input data for gaps, anomalies and tracking changes that affect optimization quality. Track model fit and prediction accuracy per portfolio and flag degradation early. Maintain portfolio configurations such as funnel steps, constraints and target definitions together with the responsible teams.