Software Engineer in Machine Learning - Core ML
Santa Clara Valley (Cupertino), California, United States
Software and Services
We believe in transforming your smartphone into a device that learns and anticipates your needs without sacrificing your privacy. We create groundbreaking technology used by teams around Apple to infuse products with intelligence. In this role you’ll work with a small and independent team to solve hard problems that improve the lives of millions of customers. You'll tackle new and hard problems in areas including data extraction, encoding and machine learning runtime performance optimizations. You'll ship code that runs on the devices you use every single day and powers products that are critical to the lives of millions of users. We’re looking for a top-tier developer that is creative, talented, and passionate about great products and fast, iterative development. You enjoy drilling down into performance optimization, moving up to API design, all the way to creating your own frameworks. If you’ve worked with Apple APIs like UIKit, Cocoa Touch, Cocoa, CoreFoundation — that’s a plus.
- Direct contribution to the design and implementation of innovative products that were successfully shipped and used by consumers
- Experience building large-scale consumer facing software
- Experience with machine learning a plus
- Experience in iOS development with C, C++, Objective-C, or Swift a plus
Work includes: Building and maintaining high-performance systems that ship on all device classes (iOS, macOS, tvOS, watchOS). Ensuring high quality and agility with unit and integration tests. You will possess strong skills in object-oriented software design and programming. You are excited about developing new features, maintaining existing code, and contributing to overall system design. Performance analysis and tuning will also be a significant responsibility in your job.
Education & Experience
BS or MS in Computer Science (or a related field), and 3 to 5 years of relevant industry experience with a proven track record.