Machine Learning Engineer I

Gen Digital•Mountain View, CA

11d

About The Position

Our team is a core part of Gen’s AI transformation. We build machine learning solutions that improve customer growth, retention, personalization, pricing, recommendations, billing success, and long-term customer value. We are looking for a hands-on AI / Machine Learning Engineer I to build models, analyze customer and product data, evaluate experiments, and help deploy practical ML solutions. You will own well-scoped projects and collaborate with experienced team members and cross-functional partners. Experience with recommender systems, uplift modeling, contextual bandits, pricing, or lifecycle personalization is a plus.

Requirements

Two or more years of professional experience in applied machine learning, data science, ML engineering, applied statistics, or a related field, including experience building and evaluating models with real-world data.
Experience analyzing behavioral, transactional, product, marketing, or customer data and translating findings into practical insights or recommendations.
Experience defining success metrics, analyzing experiments, evaluating model performance, and interpreting business impact.
Experience working with engineering, product, analytics, or business partners to deploy or apply data-driven solutions.
Strong Python skills and practical knowledge of supervised learning, model selection, hyperparameter tuning, evaluation, and performance analysis.
Strong SQL skills and experience using platforms such as BigQuery, Spark, or similar tools for data extraction, cleaning, preprocessing, exploration, and feature development.
Strong analytical and statistical reasoning, including A/B testing, holdout design, statistical significance, incrementally, and business-impact measurement.
Familiarity with common ML libraries, cloud data or ML platforms, version control, and AI-assisted development tools.
Takes responsibility for assigned work, follows through on commitments, and proactively addresses issues.
Connects modeling and analysis to customer experience and measurable outcomes.
Enjoys modeling, analyzing, automating, and shipping while using AI tools to improve productivity and quality.
Learns quickly, seeks feedback, and continuously develops technical and business knowledge.
Communicates ideas, assumptions, results, and challenges effectively with technical and non-technical partners.

Nice To Haves

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

Responsibilities

Applied ML ownership: Own well-defined machine learning projects from data exploration and model development through validation, deployment, and iteration.
Model development: Build and improve predictive, recommendation, ranking, segmentation, uplift, and customer-value models for customer personalization and decisioning.
Data and feature development: Prepare datasets, define modeling targets, develop features, and ensure data quality for training and evaluation.
Experimentation and measurement: Design and analyze A/B tests, holdouts, and offline evaluations to measure model performance and business impact.
Deployment and collaboration: Work with engineering, product, analytics, and business partners to integrate models into production and improve them based on results and feedback.
AI-first development: Use AI coding assistants, automation, and reusable tools to improve the speed, quality, and consistency of modeling and analytical workflows.