SDE II (IOS)

An ideal candidate is who: ● Design and develop advanced iOS applications using Swift, SwiftUI, and UIKit. ● Design System: Build reusable components and extend the shared component library. ● Apply MVVM architecture, industry-standard design patterns, and software engineering best practices. ● Implement robust networking layers and ensure thread safety using async/await, Combine, or GCD/Operations. ● Optimize for performance and memory efficiency using Xcode Instruments, MetricKit, and leak detection tools. ● Utilize XCTest, XCUITest, Earl grey, and snapshot testing frameworks to develop highly maintainable, testable, and automated code. ● Write End to End Automation — unit and snapshot coverage plus the on-device UI journeys for the flows you own. ● Collaborate with designers and backend engineers to ensure pixel-perfect UI and seamless API integration (REST). ● Implement push notifications, background tasks, and offline data persistence with Core Data or SwiftData. ● Participate in code reviews, CI/CD integration (e.g., Fastlane,Jenkins, Xcode Cloud), and app store delivery workflows. ● Follow Apple’s Human Interface Guidelines and ensure accessibility, localization, and performance best practices. ● Continuously explore and adopt new frameworks like App Intents, WidgetKit, Live Activities, and Dynamic Island. ● Strong communication skills, with the ability to explain complex technical issues to different audiences ● Possess exceptional problem-solving abilities and analytical thinking skills. ● Mentor Interns/SDE1 engineers through code reviews, pairing, and structured feedback — raising the technical bar of the team, not just your own output. ● Explore AI-driven capabilities — on-device ML, LLM-powered features, or AI-assisted dev tools— to enhance product experience and engineering efficiency. ● Proficient in using AI coding assistants (e.g., Claude Code) as part of the daily development workflow — for code generation, refactoring, debugging, test writing, and reviewing merge requests. ● Comfortable adopting AI-assisted engineering practices across the SDLC — from spec-to-code generation and automated test coverage to AI-driven MR review and crash/issue triage — and continuously improving how the team uses these tools.