{"schemaVersion":"jobsearcher.job.v1","id":"4d71931b3b28067fc1064ca6","url":"https://jobsearcher.com/jobs/4d71931b3b28067fc1064ca6","canonicalUrl":"https://jobsearcher.com/jobs/4d71931b3b28067fc1064ca6","title":"AI / Machine Learning Engineer II","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.\r\nTogether, 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.\r\nWhen 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.\r\nAt 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.\r\nIf this sounds like you, we’d love you to be part of Gen.\r\nAbout 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.\r\nThis 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.\r\nWe 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.\r\nKey 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.\r\nProductionization 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.\r\nExperimentation and impact measurement: Design and analyze A/B tests, holdouts, and validation frameworks to measure incremental customer and business outcomes.\r\nAdvanced model development: Design and build propensity, response, uplift, recommendation and ranking, contextual bandit, segmentation, optimization, and customer-value models.\r\nCross-functional delivery: Partner with ML infrastructure, data engineering, backend engineering, product, analytics, and business teams to integrate models into reliable production systems.\r\nAI-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.\r\nAbout 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.\r\nA Master’s or PhD in a quantitative field is a plus, but not required.\r\nExperience: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.\r\nLarge-scale data: Experience building and evaluating models using large-scale behavioral, transactional, product, marketing, or customer data.\r\nExperimentation: Experience designing experiments, defining success metrics, measuring incrementality, interpreting results, and translating findings into practical product or business decisions.\r\nProduction 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.\r\nRelevant specialization: Experience with personalization, recommendation, ranking, uplift modeling, causal inference, contextual bandits, pricing, optimization, or lifecycle decisioning is a strong plus.\r\nSkills: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.\r\nData processing and feature engineering: Strong SQL skills and experience with BigQuery, Spark, or similar platforms for data collection, cleaning, preprocessing, exploration, and feature development.\r\nAnalytics and experimentation: Strong statistical reasoning and practical knowledge of A/B testing, holdout design, causal measurement, incrementality, statistical significance, and business-impact analysis.\r\nProduction 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.\r\nPersonal Attributes:Strong ownership: Takes responsibility for delivering high-quality solutions and measurable outcomes with limited oversight.\r\nBusiness-impact orientation: Connects modeling and engineering decisions to customer experience, product performance, and business value.\r\nAI-first builder mindset: Enjoys coding, modeling, automating, and shipping while proactively using AI and agentic tools to improve productivity and quality.\r\nClear, collaborative communication: Communicates assumptions, tradeoffs, risks, and results effectively across ML, engineering, product, analytics, and business teams.\r\nWhat’s Next:Our hiring process includes the following steps:\r\nVideo Introduction: Submit a brief video introducing yourself, your work, and your most relevant experience.\r\nTechnical interview: Demonstrate your applied machine learning, analytical, and engineering capabilities.\r\nHiring manager interview: Meet with the hiring manager to discuss your background and fit for the role.\r\nFinal interview: Meet with our AI leadership, including the Chief AI Officer, for a final assessment.#J-18808-Ljbffr","company":"Socketdev","rawCompany":"socketdev","city":"Mountain View","state":"CA","isRemote":false,"isActive":true,"createdAt":"2026-08-21T01:13:56.180Z","occupations":[{"code":"15-2051.00","title":"Data Scientists","slug":"data-scientists"},{"code":"15-1221.00","title":"Computer and Information Research Scientists","slug":"computer-and-information-research-scientists"},{"code":"15-1252.00","title":"Software Developers","slug":"software-developers"}],"industries":[{"code":"541511","title":"Custom Computer Programming Services","slug":"custom-computer-programming-services"},{"code":"513210","title":"Software Publishers","slug":"software-publishers"},{"code":"541512","title":"Computer Systems Design Services","slug":"computer-systems-design-services"}],"jobPosting":{"@context":"https://schema.org","@type":"JobPosting","title":"AI / Machine Learning Engineer II","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.\r\nTogether, 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.\r\nWhen 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.\r\nAt 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.\r\nIf this sounds like you, we’d love you to be part of Gen.\r\nAbout 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.\r\nThis 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.\r\nWe 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.\r\nKey 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.\r\nProductionization 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.\r\nExperimentation and impact measurement: Design and analyze A/B tests, holdouts, and validation frameworks to measure incremental customer and business outcomes.\r\nAdvanced model development: Design and build propensity, response, uplift, recommendation and ranking, contextual bandit, segmentation, optimization, and customer-value models.\r\nCross-functional delivery: Partner with ML infrastructure, data engineering, backend engineering, product, analytics, and business teams to integrate models into reliable production systems.\r\nAI-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.\r\nAbout 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.\r\nA Master’s or PhD in a quantitative field is a plus, but not required.\r\nExperience: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.\r\nLarge-scale data: Experience building and evaluating models using large-scale behavioral, transactional, product, marketing, or customer data.\r\nExperimentation: Experience designing experiments, defining success metrics, measuring incrementality, interpreting results, and translating findings into practical product or business decisions.\r\nProduction 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.\r\nRelevant specialization: Experience with personalization, recommendation, ranking, uplift modeling, causal inference, contextual bandits, pricing, optimization, or lifecycle decisioning is a strong plus.\r\nSkills: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.\r\nData processing and feature engineering: Strong SQL skills and experience with BigQuery, Spark, or similar platforms for data collection, cleaning, preprocessing, exploration, and feature development.\r\nAnalytics and experimentation: Strong statistical reasoning and practical knowledge of A/B testing, holdout design, causal measurement, incrementality, statistical significance, and business-impact analysis.\r\nProduction 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.\r\nPersonal Attributes:Strong ownership: Takes responsibility for delivering high-quality solutions and measurable outcomes with limited oversight.\r\nBusiness-impact orientation: Connects modeling and engineering decisions to customer experience, product performance, and business value.\r\nAI-first builder mindset: Enjoys coding, modeling, automating, and shipping while proactively using AI and agentic tools to improve productivity and quality.\r\nClear, collaborative communication: Communicates assumptions, tradeoffs, risks, and results effectively across ML, engineering, product, analytics, and business teams.\r\nWhat’s Next:Our hiring process includes the following steps:\r\nVideo Introduction: Submit a brief video introducing yourself, your work, and your most relevant experience.\r\nTechnical interview: Demonstrate your applied machine learning, analytical, and engineering capabilities.\r\nHiring manager interview: Meet with the hiring manager to discuss your background and fit for the role.\r\nFinal interview: Meet with our AI leadership, including the Chief AI Officer, for a final assessment.#J-18808-Ljbffr","datePosted":"2026-08-21T01:13:56.180Z","dateModified":"2026-08-21T01:13:56.180Z","hiringOrganization":{"@type":"Organization","name":"Socketdev","sameAs":"https://jobsearcher.com"},"jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Mountain View","addressRegion":"CA","addressCountry":"US"}},"identifier":{"@type":"PropertyValue","name":"JobSearcher","value":"4d71931b3b28067fc1064ca6"},"url":"https://jobsearcher.com/jobs/4d71931b3b28067fc1064ca6"}}