Software Engineer - System and DevOps, AMP Data Science & Analytics
Austin Metro Area, Texas, United States
Software and Services
At Apple, great ideas have a way of becoming great products, services, and customer experiences very quickly. If you are an ambitious, high-energy individual who is not afraid of challenges, we’re looking for you. Apple is seeking an expert Software Engineer who is passionate about Machine Learning related applications for Apple Media Products (AMP), covering the App Store, Arcade, Apple Music/iTunes, Video, TV and other services to join AMP Data Science & Analytics team. You will dive deep into AMP’s internet-scale data to build machine learning services and products that will improve the entire analytics efforts.
- 1+ years of experience working in Software Engineer related role with strong analytical and problem solving skills.
- Good programming skills in one of the programming languages: Python, Java. And experience with software development processes such as building, unit testing, code analysis, release process, and code coverage.
- Experience working on Unix/Linux based platforms.
- Experience with relational or distributed database/file systems such as Oracle, Teradata, PostgreSQL, HDFS.
- Experience with CI/CD process as well as platforms (e.g. Jenkins) and workflow automation service (e.g., Airflow) is a plus.
- Experience for optimizing Spark application preferred.
- Excellent communication skills with meticulous attention to detail.
- Good team player.
We are looking for conscientious candidates with strong engineering knowledge to support us for building end-to-end Machine Learning related products and services. This role will work on providing engineering as well as infrastructure support to our large-scale system and making sure our services and applications are automated and reliable. This role is essential for the long-term engineering excellence of our system and you will also have the opportunities to integrate popular open sourced solutions or internal applications into our environment and make impact. The team’s culture is centered around rapid iteration with open feedback and debate along the way, plus strong collaboration with data science and business partners to deliver sound engineering solutions.