Summary

ML Researcher / Engineer wanted to join a high-impact AI/ML team in performance marketing. The company runs campaigns across 25+ verticals, and you'll be researching, inventing, and implementing suitable ML models into campaign management, and porting successful pipelines and processes into a fully automated ML-driven system. Your models will influence real bidding, real budgets, and real campaign decisions.

What's the project about?

You'll be working with ML models for a performance-based (pay-per-lead) marketing platform that relies on prediction, scoring, and ranking. You’ll work with rich tabular data, build models that power optimization loops, and collaborate closely with business and Data Engineering teams to prepare your models for production, not just notebooks.

Required Skills:

• Experience building and validating tabular ML models
• XGBoost/LightGBM/CatBoost and similar models, corresponding Python libraries
• Defining metrics and design validation setups
• Confident feature engineering and data quality/wrangling
• Data pipelines

Would be a Plus:

• Experience with model serving (FastAPI, Bento, TorchServe)
• Monitoring: drift, data checks, stability
• Background in ranking/optimization models

What will you do?

• Build predictive, scoring, and ranking models directly impacting client campaigns and ROI
• Own dataset preparation, pipelines, processes, and validation strategies
• Design experiments that challenge assumptions and uncover real lift
• Collaborate with Software and Data Engineers on feature availability and consistency, packaging, and preparing models for real usage in production

Who you are?

You have strong, hands-on experience with tabular ML (XGBoost, LightGBM, CatBoost), you’ve trained and validated models, and you enjoy shaping experiments that prove whether a hypothesis lives or dies. You think clearly, value clean data, honest metrics, and production impact.