We are a diverse collection of thinkers and doers, continually reimagining our products, systems, and practices to help people do what they love in best user friendly efficient way. Apple is a deeply collaborative place, where everything we build is the result of people in different roles and teams working together to make each others ideas stronger. That same passion for innovation that goes into our products also applies to our practices, strengthening our commitment to leave the world better than we found it.
AIML Data Infrastructure provides the foundation and core technologies all higher level abstractions run on. We strive to provide the best experience to our stakeholders. Our objective is to provide our engineers and data scientists a highly stable, reliable and performant platform to perform both analytics and data engineering activities on. Our goal is simple, provide a world class infrastructure to enable our engineers and data scientists to do their best work.
Are you a passionate about automation, optimizations and tuning? Come join us and be part of the Data Infrastructure journey.
You will be responsible for the ingest platform used by analytics and machine learning of worldwide Siri events. We manage a triple digit billion events per day. To run our environment efficiently, we drive for proper monitoring, alerting and automation. The teams goal is to ensure the reliability and performance at the highest level.
As a member of the AIML Data Infrastructure Ingest Team:
You will manage one of Apples largest infrastructure supporting hundreds of millions of Siri customers
You will diagnose, fix, improve, and automate complex issues across the entire stack to ensure maximum uptime and performance
You will take part in designing and building out our next generation elastic ingest infrastructure and push our services to the next level
You will advise other teams (within and outside of Siri) on proper integration of our platform
You will manage the ingest infrastructure which feeds all ETL, Analytics, and Privacy Efforts within AIML
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