ironquill.tech/board

$ cat jobs/ios-software-engineer-bjak-53e44c60c320.json

iOS Software Engineer

bjak·EU·Sweden·mid
swiftios
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About A1 There are over 5 billion users using basic applications today such email, notes, tasks that are not AI-native. Our mission is to build a proactive smart assistant for everyday users to bring intelligence to conversations, errands, organising and workflows, with minimal prompting. Our product focuses on achieving high reliability for long-running workflows, persistent context, and real-world task completion. The system must handle multi-step reasoning, interact with external tools, and remain reliable despite non-deterministic model behavior. Our objective is to help users complete tasks daily enjoyable with over ~90%* reduced time. Role As an iOS Software Engineer, you own the iOS client experience, how AI feels, responds, and performs in users’ hands. This is not a thin integration role. You will build a production-grade iOS application where AI interactions are central to product experience, and performance, reliability, and user trust are critical. Focus Build and maintain iOS applications using Swift and SwiftUI. Integrate AI-powered features through backend APIs. Design UX patterns for AI interactions, including loading states, streaming responses, retries, and fallbacks. Optimize performance, memory usage, and battery efficiency. Capture user signals and feedback to support AI evaluation and iteration. Collaborate closely with backend and ML engineers on API design and system behavior. Maintain high App Store quality, stability, and production reliability. Ideal Experiences 3+ years of iOS development experience using Swift. Hands-on experience integrating AI-powered features into mobile apps. Strong understanding of async/await, concurrency, and background tasks. Solid iOS performance and memory optimization skills. Experience shipping and maintaining production iOS apps. Comfort designing for latency, partial failure, and non-deterministic AI behavior. Exposure to CoreML or light on-device ML. Familiarity with feature flags or remote configuration s

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