Machine Learning Engineer, Maps
- Cupertino, CA
Posted: Jun 10, 2020
Role Number: 200061218
At Apple Maps, our mission is to build and maintain an accurate digital representation of the entire Globe. It is one of the most challenging large-scale problems in the world. We ingest a staggering amount of data and wish to discover new ways to use that data to improve the customer experience. Maps POI ML Engineering team collaborates with the data science teams to productionize, scale and deploy machine learning models that improve the quality of Points of Interests data in Apple Maps. In this role, you would enjoy working in highly technical team, where everyone learns from each other's unique skills and knowledge. Your bias for action means you thrive in an environment where you can get things done. We encourage and respect critical thinking. Challenge yourself, challenge the data, challenge us.
- ML engineering or data engineering background with more than 5 years of industry experience
- Strong coding skills in Python, Scala and/or Java
- Track record of designing, implementing and deploying scalable, performant ML or ETL pipelines
- Hands-on experience with machine learning frameworks and libraries (e.g. Tensorflow and scikit-learn) is a plus
- Strong understanding of the system design and inner workings of the systems
- Strong communication and analytical skills
The successful candidate will join our POI Machine Learning Engineering team. We're responsible for leveraging statistical models and machine learning to identify and fix data inconsistencies of our digital maps with the real world. You rigorously demonstrate coding best practices, while moving flexibly among technologies and programming languages. You will design and implement statistical and machine learning workflows on massive data. You will work cross-functionally to drive platform architecture and design, analyzing data, and producing insights. You will coordinate with data scientists to facilitate experimentation/prototyping.
Education & Experience
Masters or PhD in Computer Science, Electrical Engineering, Statistics or related field
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