Senior Machine Learning Engineer

GermanyRemotesenior

Machine Learning EngineerData

0 views0 saves0 applied

Apply Now

Quick Summary

Key Responsibilities

Take ownership of machine learning problems from concept to production Design, build, and deploy predictive models (e.g. pricing, bidding, optimization,

Requirements Summary

5+ years of experience in machine learning / applied ML roles with production ownership Proven track record of deploying and maintaining ML systems in real-world environments Strong Python skills (e.

Technical Tools

Machine Learning EngineerData

We are looking for a Senior Machine Learning Engineer to design, own, and scale predictive systems that power VIS.X - programmatic advertising platform.
You will take end-to-end responsibility for high-impact ML initiatives (e.g., pricing optimization, bid prediction, performance forecasting, delivery optimization) and translate complex business problems into robust, production-grade machine learning systems.
This is a senior individual contributor role with leadership potential. You will help shape our ML architecture, standards, and long-term AI strategy, with the opportunity to grow into a team lead role as we expand our data science capabilities.

Requirements

~1 min read

5+ years of experience in machine learning / applied ML roles with production ownership
Proven track record of deploying and maintaining ML systems in real-world environments
Strong Python skills (e.g., pandas, scikit-learn, PyTorch/TensorFlow)
Solid knowledge of statistics, experimentation design, and model evaluation
Experience working with large-scale datasets and performance-critical systems
Understanding of MLOps principles (model lifecycle, monitoring, CI/CD integration, retraining pipelines)
Strong problem ownership mindset - ability to independently structure ambiguous challenges
Ability to translate business trade-offs into modeling decisions
Experience in AdTech, marketplaces, or auction-based systems is a plus
Experience working in high-scale, real-time systems is a plus

Responsibilities

~1 min read

→Take ownership of machine learning problems from concept to production
→Design, build, and deploy predictive models (e.g. pricing, bidding, optimization, forecasting)
→Develop scalable feature engineering and data pipelines for large-scale datasets
→Define experimentation frameworks (A/B testing, offline validation, model comparison)
→Ensure production-grade MLOps: monitoring, retraining, drift detection, reliability
→Collaborate closely with DevOps, Product, Engineering teams to align ML with business impact
→Quantify model impact on revenue, margin, and performance KPIs
→Contribute to building our long-term ML architecture and best practices