Staff Machine Learning Engineer

Quick Facts

• Role: Staff Machine Learning Engineer (Machine Learning Engineering)
• Focus: AI advertising optimization using predictive lifetime value (pLTV) and calibrated signals
• Main outputs: calibrated model predictions and automated ML signals sent to advertising platforms

Description

Build and maintain predictive lifetime value models and calibration layers for an AI-powered advertising optimization platform. You will own feature engineering in BigQuery, identity resolution, monitoring for drift and causal contamination, and production delivery of ML-generated signals.

Responsibilities

• Build and maintain tiered pLTV models using XGBoost, CatBoost, and BG/NBD (routing users by data availability/observability).
• Implement model calibration layers using scalars and normalization (linear/log/rank-based) and asymmetric loss functions.
• Own feature engineering pipelines in BigQuery (Gold-layer feature tables from Silver-layer data: RFM, behavioral embeddings, category affinity, purchase velocity).
• Evaluate feature quality (completeness, missing-value monitoring) and exclude high-missing features for new users.
• Monitor model drift and distribution shifts; log residuals; compare pre/post deployment cohorts; detect causal contamination in retraining.
• Maintain batch Union-Find identity resolution in BigQuery to connect GA4 identifiers with Shopify transactional identifiers.
• Develop production-grade Python applications running on Google Cloud Run Jobs; orchestrate workflows with Apache Airflow; deliver signals to advertising platforms.
• Implement automated testing, CI/CD, idempotent processing, and dry-run safety mechanisms to protect production stability and performance.

Requirements

• 5+ years of hands-on ML Engineering with Python for production-grade model development, deployment, and maintenance.
• Experience with gradient boosting algorithms (XGBoost, CatBoost, or LightGBM), including hyperparameter tuning and advanced model evaluation.
• Strong SQL and BigQuery skills for complex feature engineering queries, scheduled transformations, and data validation.
• Hands-on GCP experience deploying and maintaining ML workloads: Cloud Run, Airflow/Composer, Vertex AI, Pub/Sub.
• Understanding of model calibration and business-oriented loss functions.
• Experience integrating ML outputs with external systems through APIs (e.g., advertising platforms, recommendation engines, pricing, bidding).
• English level: Upper-Intermediate or higher.

Benefits

• Work on a high-impact, reliable project supporting international clients.
• Access to training and a knowledge base covering technical and business topics.
• Compensation program for international professional certificates and access to BA meetups.
• Opportunity to mentor others and grow in technical or management tracks.