{"schemaVersion":"jobsearcher.job.v1","id":"ff5eb7e5e8589ecfac8e5f98","url":"https://jobsearcher.com/jobs/ff5eb7e5e8589ecfac8e5f98","canonicalUrl":"https://jobsearcher.com/jobs/ff5eb7e5e8589ecfac8e5f98","title":"Software Engineer, GenAI Frameworks","description":"Meta is building the infrastructure and systems that power machine learning at scale across its family of products, including Feed, Reels, Ads ranking, and generative AI services. The Systems ML Engineering team sits at the intersection of ML and systems software, designing and optimizing the training and inference pipelines, distributed execution frameworks, and data processing systems that enable researchers and product teams to iterate quickly and deploy reliably. In this role, you will contribute to the full lifecycle of ML systems software — from designing scalable data pipelines and distributed training infrastructure to optimizing model serving latency and throughput — directly impacting the quality and speed of ML-powered experiences for billions of people.\n\nSoftware Engineer, GenAI Frameworks Responsibilities:\n\nDesign and implement scalable systems for distributed ML training and inference, including data ingestion pipelines, feature processing, and model serving infrastructure\nDevelop and optimize ML platform components such as training orchestration, gradient communication, and checkpoint management across large-scale distributed environments\nProfile and diagnose performance bottlenecks across the ML stack, including data loading, preprocessing, forward and backward passes, and serving latency\nWrite automated tests covering expected behaviors, failure modes, and error paths for ML systems components, and build monitoring and alerting for production anomalies\nCollaborate with ML researchers and product engineers to translate model requirements into reliable, high-throughput system designs\nOwn technical design for features and components within ML infrastructure, evaluating trade-offs between throughput, latency, cost, and engineering maintainability\nParticipate in staged rollouts of ML system changes using feature flagging and A/B testing frameworks, monitoring key metrics and responding to regressions\nContribute to code quality through code reviews, clear technical documentation, and consolidation of duplicative implementations across the ML systems codebase\nSupport on-call rotations for owned ML infrastructure, investigating production incidents and contributing detailed retrospectives to prevent recurrence\n\nMinimum Qualifications:\n\nCurrently has, or is in the process of obtaining a Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience. Degree must be completed prior to joining Meta\n2+ years of experience in software engineering with a focus on machine learning systems, distributed systems, or high-performance data infrastructure\nExperience writing production-quality code in Python and at least one compiled language such as C++ or Java, including performance-sensitive systems code\nExperience designing and implementing components of ML training or inference pipelines, including data preprocessing, model execution, or serving systems\nExperience with distributed computing concepts such as data parallelism, model parallelism, or parameter server architectures as applied to ML workloads\nExperience writing automated tests, building logging and alerting, and participating in production incident response for large-scale systems\n\nPreferred Qualifications:\n\nExperience optimizing ML workloads for throughput and latency, including profiling GPU or CPU utilization, memory bandwidth, and communication overhead in distributed training or inference settings\nExperience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews)\nExperience with stream or batch data processing systems such as Apache Spark, Flink, or Kafka in the context of ML feature pipelines\nDemonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements)\nDemonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies\n1+ years production experience in GenAI post-training, RLHF, RLVR, comms/collectives, parallelism, accuracy evaluation, and performance profiling\nFamiliarity with model quantization, mixed-precision training, or other techniques for reducing compute and memory costs in production ML systems\n\nAbout Meta:\n\nMeta builds technologies that help people connect, find communities, and grow businesses. When Facebook launched in 2004, it changed the way people connect. Apps like Messenger, Instagram and WhatsApp further empowered billions around the world. Now, Meta is moving beyond 2D screens toward immersive experiences like augmented and virtual reality to help build the next evolution in social technology. People who choose to build their careers by building with us at Meta help shape a future that will take us beyond what digital connection makes possible today—beyond the constraints of screens, the limits of distance, and even the rules of physics.\n\nMeta is proud to be an Equal Employment Opportunity and Affirmative Action employer. We do not discriminate based upon race, religion, color, national origin, sex (including pregnancy, childbirth, or related medical conditions), sexual orientation, gender, gender identity, gender expression, transgender status, sexual stereotypes, age, status as a protected veteran, status as an individual with a disability, or other applicable legally protected characteristics. We also consider qualified applicants with criminal histories, consistent with applicable federal, state and local law. Meta participates in the E-Verify program in certain locations, as required by law. Please note that Meta may leverage artificial intelligence and machine learning technologies in connection with applications for employment.\n\nMeta is committed to providing reasonable accommodations for candidates with disabilities in our recruiting process. If you need any assistance or accommodations due to a disability, please let us know at accommodations-ext@meta.com.\n\n$121,992/year to $181,000/year + bonus + equity + benefits\n\nIndividual compensation is determined by skills, qualifications, experience, and location. Compensation details listed in this posting reflect the base hourly rate, monthly rate, or annual salary only, and do not include bonus, equity or sales incentives, if applicable. In addition to base compensation, Meta offers benefits. Learn more about benefits at Meta.","company":"Meta","rawCompany":"meta","city":"Menlo Park","state":"CA","isRemote":false,"isActive":false,"createdAt":"2026-09-10T09:42:41.990Z","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":"15-1221.00","title":"Computer and Information Research Scientists","slug":"computer-and-information-research-scientists"}],"industries":[{"code":"513210","title":"Software Publishers","slug":"software-publishers"},{"code":"541511","title":"Custom Computer Programming Services","slug":"custom-computer-programming-services"},{"code":"541512","title":"Computer Systems Design Services","slug":"computer-systems-design-services"}],"jobPosting":{"@context":"https://schema.org","@type":"JobPosting","title":"Software Engineer, GenAI Frameworks","description":"Meta is building the infrastructure and systems that power machine learning at scale across its family of products, including Feed, Reels, Ads ranking, and generative AI services. The Systems ML Engineering team sits at the intersection of ML and systems software, designing and optimizing the training and inference pipelines, distributed execution frameworks, and data processing systems that enable researchers and product teams to iterate quickly and deploy reliably. In this role, you will contribute to the full lifecycle of ML systems software — from designing scalable data pipelines and distributed training infrastructure to optimizing model serving latency and throughput — directly impacting the quality and speed of ML-powered experiences for billions of people.\n\nSoftware Engineer, GenAI Frameworks Responsibilities:\n\nDesign and implement scalable systems for distributed ML training and inference, including data ingestion pipelines, feature processing, and model serving infrastructure\nDevelop and optimize ML platform components such as training orchestration, gradient communication, and checkpoint management across large-scale distributed environments\nProfile and diagnose performance bottlenecks across the ML stack, including data loading, preprocessing, forward and backward passes, and serving latency\nWrite automated tests covering expected behaviors, failure modes, and error paths for ML systems components, and build monitoring and alerting for production anomalies\nCollaborate with ML researchers and product engineers to translate model requirements into reliable, high-throughput system designs\nOwn technical design for features and components within ML infrastructure, evaluating trade-offs between throughput, latency, cost, and engineering maintainability\nParticipate in staged rollouts of ML system changes using feature flagging and A/B testing frameworks, monitoring key metrics and responding to regressions\nContribute to code quality through code reviews, clear technical documentation, and consolidation of duplicative implementations across the ML systems codebase\nSupport on-call rotations for owned ML infrastructure, investigating production incidents and contributing detailed retrospectives to prevent recurrence\n\nMinimum Qualifications:\n\nCurrently has, or is in the process of obtaining a Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience. Degree must be completed prior to joining Meta\n2+ years of experience in software engineering with a focus on machine learning systems, distributed systems, or high-performance data infrastructure\nExperience writing production-quality code in Python and at least one compiled language such as C++ or Java, including performance-sensitive systems code\nExperience designing and implementing components of ML training or inference pipelines, including data preprocessing, model execution, or serving systems\nExperience with distributed computing concepts such as data parallelism, model parallelism, or parameter server architectures as applied to ML workloads\nExperience writing automated tests, building logging and alerting, and participating in production incident response for large-scale systems\n\nPreferred Qualifications:\n\nExperience optimizing ML workloads for throughput and latency, including profiling GPU or CPU utilization, memory bandwidth, and communication overhead in distributed training or inference settings\nExperience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews)\nExperience with stream or batch data processing systems such as Apache Spark, Flink, or Kafka in the context of ML feature pipelines\nDemonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements)\nDemonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies\n1+ years production experience in GenAI post-training, RLHF, RLVR, comms/collectives, parallelism, accuracy evaluation, and performance profiling\nFamiliarity with model quantization, mixed-precision training, or other techniques for reducing compute and memory costs in production ML systems\n\nAbout Meta:\n\nMeta builds technologies that help people connect, find communities, and grow businesses. When Facebook launched in 2004, it changed the way people connect. Apps like Messenger, Instagram and WhatsApp further empowered billions around the world. Now, Meta is moving beyond 2D screens toward immersive experiences like augmented and virtual reality to help build the next evolution in social technology. People who choose to build their careers by building with us at Meta help shape a future that will take us beyond what digital connection makes possible today—beyond the constraints of screens, the limits of distance, and even the rules of physics.\n\nMeta is proud to be an Equal Employment Opportunity and Affirmative Action employer. We do not discriminate based upon race, religion, color, national origin, sex (including pregnancy, childbirth, or related medical conditions), sexual orientation, gender, gender identity, gender expression, transgender status, sexual stereotypes, age, status as a protected veteran, status as an individual with a disability, or other applicable legally protected characteristics. We also consider qualified applicants with criminal histories, consistent with applicable federal, state and local law. Meta participates in the E-Verify program in certain locations, as required by law. Please note that Meta may leverage artificial intelligence and machine learning technologies in connection with applications for employment.\n\nMeta is committed to providing reasonable accommodations for candidates with disabilities in our recruiting process. If you need any assistance or accommodations due to a disability, please let us know at accommodations-ext@meta.com.\n\n$121,992/year to $181,000/year + bonus + equity + benefits\n\nIndividual compensation is determined by skills, qualifications, experience, and location. Compensation details listed in this posting reflect the base hourly rate, monthly rate, or annual salary only, and do not include bonus, equity or sales incentives, if applicable. In addition to base compensation, Meta offers benefits. Learn more about benefits at Meta.","datePosted":"2026-09-10T09:42:41.990Z","dateModified":"2026-09-10T09:42:41.990Z","hiringOrganization":{"@type":"Organization","name":"Meta","sameAs":"https://jobsearcher.com"},"jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Menlo Park","addressRegion":"CA","addressCountry":"US"}},"identifier":{"@type":"PropertyValue","name":"JobSearcher","value":"ff5eb7e5e8589ecfac8e5f98"},"url":"https://jobsearcher.com/jobs/ff5eb7e5e8589ecfac8e5f98"}}