Senior ML Engineer (Adtech)

TL;DR

Senior ML Engineer (Adtech): Developing and optimizing end-to-end machine learning solutions for audience segmentation and predictive modeling with an accent on identity-centric insights and behavioral enrichment. Focus on building production ML pipelines at petabyte scale, improving model efficiency, and translating business requirements into scalable audience intelligence.

Location: Hybrid (US-based markers identified)

Salary: $128,250 - $266,875 per year

Company

A global media and technology company building a predictive identity and data layer for over 900 million monthly active users.

What you will do

• Develop ML solutions for audience segmentation, predictive modeling (Lookalike, Propensity, Churn), and behavioral enrichment at a 2.5B+ profile scale.
• Build production pipelines for training and deploying ML models using GCP infrastructure including Vertex AI, Dataflow, and Composer.
• Design robust feature engineering pipelines utilizing Apache Spark, Beam, and BigQuery.
• Implement monitoring solutions to track model performance, data drift, and prediction quality.
• Collaborate with Data Science and Product teams to productionize research models and prototypes.
• Optimize model efficiency, inference latency, and resource utilization to reduce computational costs.

Requirements

• 5+ years of software engineering experience building production systems.
• 3+ years in ML engineering, data science, or applied machine learning roles.
• 2+ years of experience implementing and deploying ML models to production at scale.
• Hands-on experience with GCP (BigQuery, Dataproc, Composer, Dataflow, Vertex AI) or AWS equivalents.
• Strong proficiency in Python and/or Java.
• Experience with ML frameworks: TensorFlow, PyTorch, scikit-learn, or XGBoost.

Nice to have

• Prior experience in adtech, marketing technology, or consumer analytics platforms.
• Knowledge of privacy-preserving ML techniques and compliance (GDPR, CCPA).
• Familiarity with MLOps tools such as MLflow, Kubeflow, or Weights & Biases.
• Experience with online learning, real-time model serving, or feature streaming.

Culture & Benefits

• Flexible hybrid work options with occasional in-person events.
• Comprehensive benefits including healthcare and a 401k plan.
• Backup childcare and education stipends.
• Inclusive environment with 11 employee resource groups (ERGs) to foster belonging.