Senior Real-Time ML Engineer - AdTech & Bidding

Sigma Software; Województwo mazowieckie; On-site; PLN 180,000 - 280,000; Full time

Job summary

Sigma Software is seeking a Senior Machine Learning Engineer to build production-grade ML models for a large-scale AdTech platform. You will design real-time decisioning, calibration, and counterfactual evaluation systems, enabling optimized campaigns and audience targeting.

You will work with a team of data scientists and engineers to deploy scalable ML pipelines, perform rigorous offline and online validation, and contribute to platform architecture decisions to support growth and performance.

Qualifications

• 5+ years of experience in Machine Learning or Data Science with production-grade models.
• Strong Python skills including numpy, pandas, and scikit-learn.
• Strong SQL skills and experience working with large-scale datasets.
• Hands-on experience with XGBoost, LightGBM, or CatBoost.
• Experience with experimentation frameworks and A/B testing methodologies.

Responsibilities

• Build predictive modeling and optimization solutions for live ad exchange environments.
• Develop real-time bid optimization and calibration models.
• Contribute to offline evaluation frameworks and counterfactual evaluation.
• Collaborate with data science teams to deploy scalable ML pipelines.
• Prepare technical documentation and knowledge transfer materials.
• Participate in architecture discussions and design scalable ML platform components.

Skills

Python

SQL

ML production

XGBoost/LightGBM/CatBoost

Statistical testing

Experimentation

Spark / PySpark

A/B testing

SHAP explainability

English proficiency

Tools

Spark

Vertex AI

Job description

Sigma Software is seeking a Senior Machine Learning Engineer to build production-grade ML models for a large-scale AdTech platform. You will design real-time decisioning, calibration, and counterfactual evaluation systems, enabling optimized campaigns and audience targeting.

You will work with a team of data scientists and engineers to deploy scalable ML pipelines, perform rigorous offline and online validation, and contribute to platform architecture decisions to support growth and performance.