{"schemaVersion":"jobsearcher.job.v1","id":"df3ec8ae8bdc55ebb508541f","url":"https://jobsearcher.com/jobs/df3ec8ae8bdc55ebb508541f","canonicalUrl":"https://jobsearcher.com/jobs/df3ec8ae8bdc55ebb508541f","title":"Software Engineer, Machine Learning","description":"Overview\nIn this role you apply strong machine learning expertise to enhance user signals, representations, and ranking within AppLovin’s advertising platform. You will work on large-scale ML problems spanning signals, retrieval, and optimization to boost relevance and performance for a global user base. Collaborating with engineering and product teams, you’ll push experimentation and production deployment from feature development to scalable ML systems. This is an opportunity to shape how ads reach and engage users at scale and drive measurable business impact.\n\nCompensation / BenefitsHealth Insurance: Medical, Dental, Vision401(k) Retirement PlanUnlimited Discretionary Time Off10 paid holidays per year80 hours Paid Sick LeaveEquity eligible\nResponsibilitiesDevelop and improve user signals, features, and representations for large-scale ML models used in advertising and recommendationsExplore ML approaches to learn from large-scale, sparse, noisy, heterogeneous signalsImprove signal quality and usage and measure impact on downstream models and advertising performanceDevelop user representations and modeling approaches for ranking, retrieval, prediction, and optimizationAdvance large-scale recommendation systems across candidate retrieval, ranking, prediction, and optimizationExplore new model architectures and learning approaches to improve recommendation quality and advertising performanceDevelop scalable approaches for representation learning, feature interaction, and multi-task learning over large-scale user signalsIdentify and solve ML problems spanning signal quality, feature quality, model quality, training stability, data integrity, and serving performanceScale ML models and training systems to support increasing data volume and complexityImprove training and inference efficiency by diagnosing bottlenecks in computation, data loading, memory, and hardware utilizationBuild scalable tools and frameworks for evaluation, training, experimentation, deployment, monitoring, and debuggingDesign and analyze offline and online experiments to understand incremental value of signals and model improvements and their impact on product outcomesCollaborate with engineering, data, and product teams to bring signals and ML approaches from experimentation into production\nKey requirementsBachelor's degree in Computer Science, Computer Engineering, Machine Learning, or related field, or equivalent practical experience4+ years of experience deploying ML systems in productionExperience with ML or DL in recommendation, ranking, retrieval, prediction, advertising, representation learning or relatedExperience training ML models on large-scale datasetsStrong fundamentals in ML architectures, optimization, representation learning, feature engineering, and evaluationStrong programming and software engineering skills for reliable production systemsExperience with PyTorch or TensorFlowExperience diagnosing data/feature quality, model quality, training, or serving performancePyTorchTensorFlowlarge-scale ML systemsrepresentation learningfeature engineeringdistributed training","company":"Applovin","rawCompany":"applovin","city":"San Jose","state":"CA","isRemote":false,"isActive":false,"createdAt":"2026-09-15T03:40:20.602Z","occupations":[{"code":"15-1252.00","title":"Software Developers","slug":"software-developers"},{"code":"15-1299.08","title":"Computer Systems Engineers/Architects","slug":"computer-systems-engineers-architects"},{"code":"15-2051.00","title":"Data Scientists","slug":"data-scientists"}],"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":"Software Engineer, Machine Learning","description":"Overview\nIn this role you apply strong machine learning expertise to enhance user signals, representations, and ranking within AppLovin’s advertising platform. You will work on large-scale ML problems spanning signals, retrieval, and optimization to boost relevance and performance for a global user base. Collaborating with engineering and product teams, you’ll push experimentation and production deployment from feature development to scalable ML systems. This is an opportunity to shape how ads reach and engage users at scale and drive measurable business impact.\n\nCompensation / BenefitsHealth Insurance: Medical, Dental, Vision401(k) Retirement PlanUnlimited Discretionary Time Off10 paid holidays per year80 hours Paid Sick LeaveEquity eligible\nResponsibilitiesDevelop and improve user signals, features, and representations for large-scale ML models used in advertising and recommendationsExplore ML approaches to learn from large-scale, sparse, noisy, heterogeneous signalsImprove signal quality and usage and measure impact on downstream models and advertising performanceDevelop user representations and modeling approaches for ranking, retrieval, prediction, and optimizationAdvance large-scale recommendation systems across candidate retrieval, ranking, prediction, and optimizationExplore new model architectures and learning approaches to improve recommendation quality and advertising performanceDevelop scalable approaches for representation learning, feature interaction, and multi-task learning over large-scale user signalsIdentify and solve ML problems spanning signal quality, feature quality, model quality, training stability, data integrity, and serving performanceScale ML models and training systems to support increasing data volume and complexityImprove training and inference efficiency by diagnosing bottlenecks in computation, data loading, memory, and hardware utilizationBuild scalable tools and frameworks for evaluation, training, experimentation, deployment, monitoring, and debuggingDesign and analyze offline and online experiments to understand incremental value of signals and model improvements and their impact on product outcomesCollaborate with engineering, data, and product teams to bring signals and ML approaches from experimentation into production\nKey requirementsBachelor's degree in Computer Science, Computer Engineering, Machine Learning, or related field, or equivalent practical experience4+ years of experience deploying ML systems in productionExperience with ML or DL in recommendation, ranking, retrieval, prediction, advertising, representation learning or relatedExperience training ML models on large-scale datasetsStrong fundamentals in ML architectures, optimization, representation learning, feature engineering, and evaluationStrong programming and software engineering skills for reliable production systemsExperience with PyTorch or TensorFlowExperience diagnosing data/feature quality, model quality, training, or serving performancePyTorchTensorFlowlarge-scale ML systemsrepresentation learningfeature engineeringdistributed training","datePosted":"2026-09-15T03:40:20.602Z","dateModified":"2026-09-15T03:40:20.602Z","hiringOrganization":{"@type":"Organization","name":"Applovin","sameAs":"https://jobsearcher.com"},"jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"San Jose","addressRegion":"CA","addressCountry":"US"}},"identifier":{"@type":"PropertyValue","name":"JobSearcher","value":"df3ec8ae8bdc55ebb508541f"},"url":"https://jobsearcher.com/jobs/df3ec8ae8bdc55ebb508541f"}}