{"schemaVersion":"jobsearcher.job.v1","id":"ac8e227fec376111b8bb2375","url":"https://jobsearcher.com/jobs/ac8e227fec376111b8bb2375","canonicalUrl":"https://jobsearcher.com/jobs/ac8e227fec376111b8bb2375","title":"Research Engineer - Machine Learning","description":"Reality Labs (RL) is Meta’s innovation engine for next-generation AR/VR, AI, and wearable technologies. Our Audio team pioneers research and development at the intersection of sound, machine learning, and human experience—enabling new ways for people to connect, communicate, and collaborate.We are seeking an experienced Research Engineer specializing in Machine Learning Infrastructure to join our Reality Labs Audio team. You will be a technical leader supporting research and product development for AI wearables as part of Meta’s Superhuman Communication & Connection initiative. Your work will focus on building and optimizing ML infrastructure, including data pipelines, model training, evaluation, and validation—leveraging both companywide platforms and developing custom developed project-specific tools.By joining our team, you will have the opportunity to work on breakthrough technologies that redefine how people connect and communicate, collaborate with world-class researchers and engineers in a fast-paced, mission-driven environment, and shape the future of AI wearables and superhuman audio experiences.\n\nResearch Engineer - Machine Learning Responsibilities:\n\nDesign, implement, and maintain software and hardware pipelines for biosignal and audio data collection, processing, and analysis\nCollaborate closely with research scientists to translate experimental algorithms and prototypes into robust, scalable engineering solutions\nDevelop tools and infrastructure for data management, annotation, and visualization to accelerate research workflows\nWork cross-functionally with hardware, software, ML, and UX teams to deliver end-to-end solutions\nMentor less experienced engineers and contribute to technical execution\nDocument engineering processes and best practices for knowledge sharing and reproducibility\nArchitect, implement, and maintain scalable data pipelines for biosignal and audio data ingestion, processing, and storage\nBuild and optimize infrastructure for large-scale model training, hyperparameter tuning, and distributed computing\nDevelop robust systems for model evaluation, validation, and deployment, ensuring reproducibility and reliability\nIntegrate companywide ML platforms with project specific tools to accelerate research and product workflows\nDevelop dashboards and visualization tools for monitoring data and model performance\nIntegrate biosignal and audio processing modules into wearable device platforms\nOptimize system performance for real-time operation, reliability, and scalability\nSupport deployment of machine learning models for biosignal interpretation and audio enhancement\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\nBachelor’s or Master’s degree in Computer Science, Electrical Engineering, or related field\n2+ years of experience in machine learning infrastructure, data engineering, or related roles\nDemonstrated programming skills in Python with a focus on ML training\nExperience with common ML training platforms (e.g., Pytorch, Tensorflow)\nProven track record in building scalable data pipelines and ML training/evaluation systems\nFamiliarity with cloud computing, distributed systems, and large-scale data management\nDemonstrated problem-solving, communication, and collaboration skills\n\nPreferred Qualifications:\n\nExperience with biosignal and audio data processing for wearable devices\nHands on experience optimizing models for on-device, hardware inference\nBackground in AR/VR, multimodal sensing, or human-computer interaction\nExperience integrating experimental research code into production infrastructure\nKnowledge of best practices for ML model validation, monitoring, and deployment\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$58.65/hour 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":"Oculus","rawCompany":"oculus","city":"Redmond","state":"WA","isRemote":false,"isActive":false,"createdAt":"2026-04-14T11:17:13.992Z","occupations":[{"code":"15-1221.00","title":"Computer and Information Research Scientists","slug":"computer-and-information-research-scientists"},{"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":"541715","title":"Research and Development in the Physical, Engineering, and Life Sciences (except Nanotechnology and Biotechnology)","slug":"research-and-development-in-the-physical-engineering-and-life-sciences-except-nanotechnology-and-biotechnology"},{"code":"541512","title":"Computer Systems Design Services","slug":"computer-systems-design-services"},{"code":"541990","title":"All Other Professional, Scientific, and Technical Services","slug":"all-other-professional-scientific-and-technical-services"}],"jobPosting":{"@context":"https://schema.org","@type":"JobPosting","title":"Research Engineer - Machine Learning","description":"Reality Labs (RL) is Meta’s innovation engine for next-generation AR/VR, AI, and wearable technologies. Our Audio team pioneers research and development at the intersection of sound, machine learning, and human experience—enabling new ways for people to connect, communicate, and collaborate.We are seeking an experienced Research Engineer specializing in Machine Learning Infrastructure to join our Reality Labs Audio team. You will be a technical leader supporting research and product development for AI wearables as part of Meta’s Superhuman Communication & Connection initiative. Your work will focus on building and optimizing ML infrastructure, including data pipelines, model training, evaluation, and validation—leveraging both companywide platforms and developing custom developed project-specific tools.By joining our team, you will have the opportunity to work on breakthrough technologies that redefine how people connect and communicate, collaborate with world-class researchers and engineers in a fast-paced, mission-driven environment, and shape the future of AI wearables and superhuman audio experiences.\n\nResearch Engineer - Machine Learning Responsibilities:\n\nDesign, implement, and maintain software and hardware pipelines for biosignal and audio data collection, processing, and analysis\nCollaborate closely with research scientists to translate experimental algorithms and prototypes into robust, scalable engineering solutions\nDevelop tools and infrastructure for data management, annotation, and visualization to accelerate research workflows\nWork cross-functionally with hardware, software, ML, and UX teams to deliver end-to-end solutions\nMentor less experienced engineers and contribute to technical execution\nDocument engineering processes and best practices for knowledge sharing and reproducibility\nArchitect, implement, and maintain scalable data pipelines for biosignal and audio data ingestion, processing, and storage\nBuild and optimize infrastructure for large-scale model training, hyperparameter tuning, and distributed computing\nDevelop robust systems for model evaluation, validation, and deployment, ensuring reproducibility and reliability\nIntegrate companywide ML platforms with project specific tools to accelerate research and product workflows\nDevelop dashboards and visualization tools for monitoring data and model performance\nIntegrate biosignal and audio processing modules into wearable device platforms\nOptimize system performance for real-time operation, reliability, and scalability\nSupport deployment of machine learning models for biosignal interpretation and audio enhancement\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\nBachelor’s or Master’s degree in Computer Science, Electrical Engineering, or related field\n2+ years of experience in machine learning infrastructure, data engineering, or related roles\nDemonstrated programming skills in Python with a focus on ML training\nExperience with common ML training platforms (e.g., Pytorch, Tensorflow)\nProven track record in building scalable data pipelines and ML training/evaluation systems\nFamiliarity with cloud computing, distributed systems, and large-scale data management\nDemonstrated problem-solving, communication, and collaboration skills\n\nPreferred Qualifications:\n\nExperience with biosignal and audio data processing for wearable devices\nHands on experience optimizing models for on-device, hardware inference\nBackground in AR/VR, multimodal sensing, or human-computer interaction\nExperience integrating experimental research code into production infrastructure\nKnowledge of best practices for ML model validation, monitoring, and deployment\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$58.65/hour 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":"2026-04-14T11:17:13.992Z","dateModified":"2026-04-14T11:17:13.992Z","hiringOrganization":{"@type":"Organization","name":"Oculus","sameAs":"https://jobsearcher.com"},"jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Redmond","addressRegion":"WA","addressCountry":"US"}},"identifier":{"@type":"PropertyValue","name":"JobSearcher","value":"ac8e227fec376111b8bb2375"},"url":"https://jobsearcher.com/jobs/ac8e227fec376111b8bb2375"}}