ML Engineer

Quick Facts

• Role: ML Engineer

Description

You will build and maintain production ML models that directly influence marketing decisions, including targeting, recommendations, and marketing budget allocation. The role covers the full pipeline: feature engineering in BigQuery, deployment on GCP, and delivering predictions to external platforms that trigger automated actions such as campaign optimization, recommendations, and pricing.

Responsibilities

• Build, deploy, and maintain production ML models in reliable, well-tested Python code
• Create features, perform data quality checks, and run scheduled transformations in BigQuery
• Deploy and operate ML workloads on GCP (Vertex AI, Pub/Sub, Cloud Run, Composer/Airflow)
• Integrate predictions with external platforms via APIs to automate actions
• Tune and evaluate gradient boosting models against business-relevant metrics
• Calibrate predictions and assess how model errors affect downstream decisions

Requirements

• Minimum 5 years of ML engineering experience in Python, including ownership of deployed production models
• Experience integrating prediction outputs into external platforms via APIs for automated actions (campaign optimization, recommendations, or pricing)
• Advanced SQL and BigQuery knowledge for feature engineering, data checks, and scheduled transformations
• Practical experience deploying ML workloads on GCP using Vertex AI, Pub/Sub, Cloud Run, Composer/Airflow
• Production experience with gradient boosting models (LightGBM, CatBoost, or XGBoost), including tuning and business-metric evaluation
• Ability to calibrate predictions and evaluate how errors impact later-stage decision-making

Benefits

Opportunity to own models end-to-end and make business-critical impact through production deployment.