AI / Machine Learning Engineer II

Gen Digital Inc.•Mountain View, CA

1d•$300,000 - $300,000

About The Position

Our team is a core part of Gen’s AI transformation. We build machine learning systems that directly improve customer growth, retention, personalization, pricing, recommendations, billing success, and long-term customer value across a large global consumer portfolio. This role focuses on applied machine learning, experimentation, and business-impact modeling. You will build practical models that personalize customer decisions across in-app messages, email, portals, billing flows, and lifecycle journeys. We are looking for a hands-on AI / Machine Learning Engineer who can frame business problems, build models, design experiments, measure impact rigorously, and partner with engineering and product teams to bring models into production. Experience with recommender systems, uplift modeling, contextual bandits, pricing, or lifecycle personalization is a strong plus.

Requirements

Applied ML experience: Five or more years of professional experience in applied machine learning, data science, ML engineering, applied statistics, or a related field, or equivalent demonstrated impact.
Large-scale data: Experience building and evaluating models using large-scale behavioral, transactional, product, marketing, or customer data.
Experimentation: Experience designing experiments, defining success metrics, measuring incrementality, interpreting results, and translating findings into practical product or business decisions.
Production collaboration and ML operations: Experience partnering with engineering, product, analytics, and business teams to deploy and operate production ML systems, including inference pipelines, monitoring, observability, retraining, and cloud-based MLOps workflows.
Strong Python skills and hands-on experience with common ML frameworks, 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 and 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.
Takes responsibility for delivering high-quality solutions and measurable outcomes with limited oversight.
Connects modeling and engineering decisions to customer experience, product performance, and business value.
Enjoys coding, modeling, automating, and shipping while proactively using AI and agentic tools to improve productivity and quality.
Communicates assumptions, tradeoffs, risks, and results effectively across ML, engineering, product, analytics, and business teams.

Nice To Haves

Experience with recommender systems, uplift modeling, contextual bandits, pricing, or lifecycle personalization is a strong plus.
Experience with personalization, recommendation, ranking, uplift modeling, causal inference, contextual bandits, pricing, optimization, or lifecycle decisioning is a strong plus.
A Master’s or PhD in a quantitative field is a plus, but not required.

Responsibilities

End-to-end ML ownership: Independently lead applied machine learning initiatives from data preparation and model development through experimentation, production deployment, monitoring, and continuous optimization.
Productionization and MLOps: Deploy and operate scalable ML solutions with robust workflows for batch or real-time inference, evaluation, monitoring, observability, versioning, retraining, rollback, and continuous model iteration.
Experimentation and impact measurement: Design and analyze A/B tests, holdouts, and validation frameworks to measure incremental customer and business outcomes.
Advanced model development: Design and build propensity, response, uplift, recommendation and ranking, contextual bandit, segmentation, optimization, and customer-value models.
Cross-functional delivery: Partner with ML infrastructure, data engineering, backend engineering, product, analytics, and business teams to integrate models into reliable production systems.
AI-first engineering workflows: Build agentic tools, automation, and reusable modules that streamline model development and MLOps workflows, improve productivity, and increase the speed, quality, and consistency of ML delivery.