Senior Machine Learning Engineer

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.

• 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

• →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