AI / Machine Learning Engineer II

Job description

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 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. Key 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. About You: Education: 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. Experience: • 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. Gen | AI / Machine Learning Engineer II • 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. • Relevant specialization: Experience with personalization, recommendation, ranking, uplift modeling, causal inference, contextual bandits, pricing, optimization, or lifecycle decisioning is a strong plus. Skills: • Machine learning and modeling: Strong Python skills and hands-on experience with common ML frameworks, supervised learning, model selection, hyperparameter tuning, evaluation, and performance diagnosis. • Data processing and feature engineering: Strong SQL skills and experience with BigQuery, Spark, or similar platforms for data collection, cleaning, preprocessing, exploration, and feature development. • Analytics and experimentation: Strong statistical reasoning and practical knowledge of A/B testing, holdout design, causal measurement, incrementality, statistical significance, and business-impact analysis. • Production engineering and MLOps: Experience with cloud ML platforms, deployment pipelines, batch or real-time inference, CI/CD, model registries, monitoring, observability, retraining, rollback, and scalable system design. Personal Attributes: • Strong ownership: Takes responsibility for delivering high-quality solutions and measurable outcomes with limited oversight. • Business-impact orientation: Connects modeling and engineering decisions to customer experience, product performance, and business value. • AI-first builder mindset: Enjoys coding, modeling, automating, and shipping while proactively using AI and agentic tools to improve productivity and quality. • Clear, collaborative communication: Communicates assumptions, tradeoffs, risks, and results effectively across ML, engineering, product, analytics, and business teams. What’s Next: Our hiring process includes the following steps: 1. Video Introduction: Submit a brief video introducing yourself, your work, and your most relevant experience. 2. Technical interview: Demonstrate your applied machine learning, analytical, and engineering capabilities. 3. Hiring manager interview: Meet with the hiring manager to discuss your background and fit for the role. 4. Final interview: Meet with our AI leadership, including the Chief AI Officer, for a final assessment.