SDE II (IOS)

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