- Develop, design, create, modify, and/or test software applications or systems for various products or software services.
- Develop a strong understanding of relevant product area, codebase, and/or systems.
- Demonstrate proficiency in data analysis, programming, and software engineering.
- Produce high-quality code with good test coverage using modern abstractions and frameworks.
- May be assigned to specific group focused on product or infrastructure.
- Work on problems of moderate scope where analysis of situations or data requires a review of a variety of factors.
- Exercise judgment within defined procedures and practices to determine appropriate action.
- Master internal development standards from developing to releasing code in order to take on tasks and projects with increasing levels of complexity.
- Requires a Bachelor's degree in Computer Science, Engineering, Information Systems, Analytics, Mathematics, Statistics, or a related field. Position requires completion of a university-level course, research project, or internship involving the following:
- 1. Coding in C, C++, Java, or C#
- 2. Relational databases and SQL
- 3. Leveraging frameworks in React or React Native
- 4. Building large-scale infrastructure applications
- 5. Creating web applications using Python, PHP, or Ruby
- 6. Implementing web interfaces using JavaScript, HTML, or CSS
- 7. Conducting design and code reviews
- 8. Building Android applications in Java using Android SDK
- 9. AOSP (Android Open Source Project)
- 10. Android internals or frameworks services
- 11. Multithreading programming and mobile memory management
- 12. Linux, UNIX, or other *nix-like OS as evidenced by file manipulation, advanced commands, and shell scripting
- 13. Building complex applications for iOS-based products using Objective-C, Swift, or C++ with the iOS SDK
- 14. Mobile architecture patterns that can scale to a large number of programmers and teams in one codebase including MVVM, VIPER, or modularization
- 15. Dependency injection and scoped access to shared resources
- 16. Machine Learning Framework(s): PyTorch, MXNet, or Tensorflow
- 17. Machine learning, recommendation systems, computer vision, natural language processing, data mining, or distributed systems
- 18. File systems, server architectures, and distributed systems
- 19. Hadoop, HBase, Pig, MapReduce, Sawzall, Bigtable, or Spark
- 20. Design scalable distributed systems with established partition tolerance, consistency, and availability guarantees
- 21. Modelling data flows between systems to enable transactional workflows and upstream or downstream integrations.
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