Senior ML Engineer - Audience Enrichment

About the Team

Our platform is the foundational identity and data layer for 900M+ monthly active users, serving 2.5B+ profiles at massive scale. We are building a predictive, identity-centric insights engine—ensuring our audience is understood with precision to deliver hyper-personalized experiences and advertising solutions across all our digital properties.

Our mission centers on first-party data strategy: capturing, enriching, and activating audience signals to build a 360-degree view of every user. We operate under a Privacy-by-Design philosophy, adhering to global regulations (GDPR, CCPA) and industry security standards, while leveraging a cloud-native stack across GCP (BigQuery, Spanner, Dataflow, Composer, GKE) and AWS, with modern MLOps practices to deliver measurable business impact.

About the Role

As a Senior ML Engineer, you will develop and optimize end-to-end machine learning solutions that transform raw user data into actionable audience intelligence. Your predictive models—including Lookalike audiences, Propensity scores, and Churn predictions—enable Product, Engineering, and Sales teams to deliver hyper-personalized experiences and maximize the commercial value of our 900M+ monthly active users.

You will build production ML pipelines processing data from our 2.5B+ profile platform, creating enrichment signals that directly impact user engagement, ad revenue, and retention. Your work requires balancing model accuracy, computational efficiency, and data privacy while operating at massive scale across petabyte-scale data infrastructure.

This role demands expertise in production ML engineering, large-scale data processing frameworks (Spark, Beam, BigQuery), and MLOps practices. You will collaborate closely with Data Science, Product, and Engineering teams to translate business requirements into scalable ML solutions that drive measurable business outcomes.

Key Responsibilities

• Develop and optimize end-to-end ML solutions for audience segmentation, predictive modeling, and behavioral enrichment at 2.5B+ profile scale
• Build reliable production pipelines for training, evaluating, and deploying ML models using GCP infrastructure (Vertex AI, Dataflow, Composer)
• Design robust feature engineering pipelines using large-scale data processing frameworks (Spark, Beam, BigQuery)
• Implement comprehensive monitoring solutions tracking model performance, data drift, prediction quality, and business impact metrics
• Tune, validate, and optimize ML models for accuracy, efficiency, and scalability while managing computational costs
• Collaborate with Data Science teams to productionize research models and translate prototypes into scalable production systems
• Partner with Product teams to understand business requirements and deliver ML capabilities for audience intelligence
• Apply ML engineering best practices including version control (Git), automated testing, CI/CD workflows, and model versioning
• Create comprehensive documentation for ML systems, feature pipelines, model artifacts, and operational runbooks
• Improve model efficiency, inference latency, and resource utilization for cost-effective production serving
• Troubleshoot data pipeline failures, model serving issues, and data quality problems in production environments
• Participate in technical discussions, code reviews, and knowledge sharing across teams

Required Qualifications

Education

• Bachelor's or Master's degree in Computer Science, Data Science, Statistics, Machine Learning, or related technical field

Experience

• 5+ years software engineering experience building production systems
• 3+ years in ML engineering, data science, or applied machine learning roles
• 2+ years implementing and deploying ML models to production environments at scale
• 2+ years hands-on experience with GCP (BigQuery, Dataproc, Composer, Dataflow, Vertex AI) or AWS equivalents

Technical Skills

• Strong Python and/or Java programming skills for production ML systems
• Proficiency with ML frameworks: TensorFlow, PyTorch, scikit-learn, XGBoost for building and deploying models
• Hands-on experience with data processing frameworks: Apache Spark, Apache Beam, or equivalent distributed computing tools
• Solid ML fundamentals: supervised/unsupervised learning, feature engineering, model evaluation, hyperparameter tuning
• SQL proficiency for large-scale data manipulation and feature extraction
• Understanding of MLOps practices: model versioning, A/B testing, monitoring, feature stores, model serving

Competencies

• Demonstrated ability translating business requirements into technical ML solutions with measurable impact
• Strong problem-solving skills and analytical thinking in complex data environments
• Excellent collaboration with cross-functional teams including product, data science, and engineering
• Team-level impact with ability to influence technical decisions within immediate team
• Understanding of data privacy compliance (GDPR, CCPA) in ML systems

Preferred Qualifications

• Experience with audience segmentation, propensity modeling, recommendation systems, or user behavior prediction
• Knowledge of privacy-preserving ML techniques and compliance requirements (GDPR, CCPA, differential privacy)
• Familiarity with MLOps tools: MLflow, Kubeflow, Vertex AI Pipelines, Weights & Biases
• Prior experience in adtech, marketing technology, or consumer analytics platforms
• Understanding of A/B testing methodologies, experimentation frameworks, and causal inference
• Experience with online learning, real-time model serving, or feature streaming
• Contributions to ML open-source projects, technical publications, or conference presentations
• Self-driven, detail-oriented, strong problem-solving abilities in fast-paced environments