Apple builds world-class innovative products that integrate hardware with intelligent software experiences using machine learning, artificial intelligence, and computer vision. The Machine Learning Platform and Technologies (MLPT) is the team that makes it possible for Apple engineers to innovate on ML driven product features rapidly and at scale by providing services designed specifically for the ML engineering lifecycle. These services are being built and optimized using a data-driven and ML approach. Come join the team that is building the data platform that drives product and service development decision making.
We are looking for exceptional engineers with experience in building distributed systems and scalable data platforms used to build data lakes and near-realtime analytics systems. Qualified candidates are experienced in building services and have experience working with data systems. They possess not only an understanding of how to build data pipelines, but have a deep understanding of how the underlying large scale data systems are designed and implemented and are on the vanguard of understanding what the latest open sourced large scale data storage and processing systems are available and how they are designed. Successful candidates have a high bias for action and are able to get build complex solutions and systems with little to no guidance. They are comfortable quickly combine multiple technologies to build systems and have the wherewithal to dive deeply into components when things are working. They sweat the details and have a strong intuitive sense for data quality.
In this role, you will partner with engineers and machine learning engineers throughout the MLPT organization. Together you will create key data systems that driving the observability and optimization of the services the team provides. You will own the definition, design, and implementation of a production-ready data service that will used for data-driven optimization and improvements of our services and evolve to support a ML driven approach to system optimization.
Ideal candidates are:
* Comfortable working in earlier stages of product development with ambiguous requirements
* Deep understanding of how modern data systems work and not just an end user of such systems
* Experienced across the software services stack including front-end systems
* Have experience and in-depth knowledge of open source distributed systems such as Hadoop, Spark, Zookeepr, etcd, Cassandra, Kubernetes
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