AI / Machine Learning Engineer II

Gen Digital seeks an AI / Machine Learning Engineer II for its Mountain View, CA location. The company operates global consumer brands including Norton, Avast, LifeLock, and MoneyLion, delivering cybersecurity, privacy, and financial wellness services to nearly 500 million users. The role centers on applied machine learning for customer growth, retention, personalization, pricing, and lifecycle value across a large portfolio, with emphasis on experimentation and production deployment.

Responsibilities

• Lead applied machine learning projects from data preparation and model development through experimentation, production deployment, monitoring, and ongoing optimization.
• Deploy and maintain scalable ML solutions using workflows for batch or real-time inference, evaluation, monitoring, versioning, retraining, rollback, and continuous iteration.
• Design and analyze A/B tests, holdouts, and validation frameworks to quantify incremental customer and business outcomes.
• Develop propensity, response, uplift, recommendation, ranking, contextual bandit, segmentation, optimization, and customer-value models.
• Collaborate with ML infrastructure, data engineering, backend engineering, product, analytics, and business teams to integrate models into production systems.
• Create agentic tools, automation, and reusable modules to streamline model development and MLOps processes.

Requirements

• Five or more years of professional experience in applied machine learning, data science, ML engineering, or applied statistics, or equivalent demonstrated impact.
• Experience building and evaluating models on large-scale behavioral, transactional, product, marketing, or customer datasets.
• Experience designing experiments, defining success metrics, measuring incrementality, and translating results into product or business decisions.
• Experience deploying and operating production ML systems with engineering, product, analytics, and business teams, including inference pipelines, monitoring, observability, retraining, and cloud-based MLOps workflows.
• Strong Python skills and hands-on experience with common ML frameworks for supervised learning, model selection, hyperparameter tuning, evaluation, and performance diagnosis.
• Strong SQL skills and experience with BigQuery, Spark, or similar platforms for data collection, cleaning, preprocessing, exploration, and feature development.
• Strong statistical reasoning with practical knowledge of A/B testing, holdout design, causal measurement, incrementality, statistical significance, and business-impact analysis.
• Experience with cloud ML platforms, deployment pipelines, batch or real-time inference, CI/CD, model registries, monitoring, observability, retraining, rollback, and scalable system design.
• Experience with personalization, recommendation, ranking, uplift modeling, causal inference, contextual bandits, pricing, optimization, or lifecycle decisioning is a plus.
• A technical degree in Computer Science, Data Science, Statistics, Mathematics, Operations Research, Economics, Engineering, or a related field is helpful; a Master’s or PhD in a quantitative field is also a plus; equivalent practical experience is valued equally.