AI & Machine Learning Engineer I

About Gen: Gen is a global company dedicated to powering Digital Freedom through its trusted consumer brands including Norton, Avast, LifeLock, MoneyLion and more. Our combined heritage is rooted in financial empowerment and cyber safety for the first digital generations, and today we deliver award-winning cybersecurity, online privacy, identity protection and financial wellness solutions to nearly 500 million users in more than 150 countries. Together, we share a collective passion and vision to protect consumers and help them grow, manage and secure their digital and financial lives. We’re always looking for smart, fearless and high-impact talent who see AI as a teammate – leveraging it to move faster and deliver meaningful results. When you’re part of Gen, you’ll have the flexibility, tools and support to do your best work and grow your career – from flexible working options and time off to competitive pay, benefits and well-being programs. At Gen, we are scrappy and relentlessly customer driven. We create room for healthy debate, experimentation and continuous learning, and we seek out people with different experiences, identities and ideas to join our team. You’ll work with people who back each other, respect each other and understand that our differences are a competitive advantage. If this sounds like you, we’d love you to be part of Gen. About the Role: 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. Key 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. About You: Degree requirements are flexible. A technical degree in Computer Science, Data Science, Statistics, Mathematics, Operations Research, Economics, Engineering, or a related field is helpful, but equivalent practical experience is equally valued. A Master’s or PhD in a quantitative field is a plus, but not required. Applied ML and model development: 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. Data analytics: Experience analyzing behavioral, transactional, product, marketing, or customer data and translating findings into practical insights or recommendations. Experimentation: Experience defining success metrics, analyzing experiments, evaluating model performance, and interpreting business impact. Collaborative delivery: Experience working with engineering, product, analytics, or business partners to deploy or apply data-driven solutions. Relevant specialization: Experience with personalization, recommendation, ranking, uplift modeling, causal inference, contextual bandits, pricing, or lifecycle decisioning is a plus. Machine learning and modeling: Strong Python skills and practical knowledge of supervised learning, model selection, hyperparameter tuning, evaluation, and performance analysis. Data processing and feature engineering: Strong SQL skills and experience using platforms such as BigQuery, Spark, or similar tools for data extraction, cleaning, preprocessing, exploration, and feature development. Analytics and experimentation: Strong analytical and statistical reasoning, including A/B testing, holdout design, statistical significance, incrementally, and business-impact measurement. Technical tools and workflows: Familiarity with common ML libraries, cloud data or ML platforms, version control, and AI-assisted development tools. Ownership mindset: Takes responsibility for assigned work, follows through on commitments, and proactively addresses issues. Business-impact orientation: Connects modeling and analysis to customer experience and measurable outcomes. AI-first builder mindset: Enjoys modeling, analyzing, automating, and shipping while using AI tools to improve productivity and quality. Growth mindset: Learns quickly, seeks feedback, and continuously develops technical and business knowledge. Clear, collaborative communication: Communicates ideas, assumptions, results, and challenges effectively with technical and non-technical partners. What’s Next: Our hiring process includes four stages:

1. Video Introduction: Submit a brief video introducing yourself, your work, and your most relevant experience.
2. Recruiter Interview: Meet with a Technical Recruiter to discuss your background and walk through the interview process.
3. Technical Interview: Demonstrate your applied machine learning, analytical, and technical capabilities.
4. Hiring Manager Interview: Meet with the hiring manager to discuss your background and fit for the role.
5. Final Interview: Meet with our AI leadership for a final assessment.