iOS Engineer_Native Biomathematical Engine Integration

Brief DescriptionPosition Type: Contract-to-hire or contract preferred; direct hire optional (candidate must own Mac required)Hourly Pay: $25 - $30Location: Remote (U.S.)Minimum Years of Experience Required: 2Role summaryExisting production iOS app with a prepared calculation protocol boundary. Integrate a licensed third-party native biomathematical library behind that boundary, map SDK I/O to app DTOs, dual-run against an in-house baseline, feature-flag cutover, and ship without rewriting product UI.Technical contextAreaStackPlatformiOS 17+, Swift 5.9+, SwiftUIArchitectureMVVM-ish + service layer (singletons + @EnvironmentObject)PersistenceCore Data; Keychain for sensitive paramsIntegrationsTestsScaleHealthKit, UserNotifications,BackgroundTasks, hapticsXCTest unit/integration + UITests~80 Swift files / ~18k LOC incl. testsProcessConventional commits; PR to main;concurrency hygiene in progress(@MainActor, Swift 6–oriented)Architecture Seam (already Prepared)SwiftUI ViewsServices (data, HealthKit, notifications, personalization, monitor)AlertnessEngine protocol ? integration pointDomain models + Core DataBoundary Already In PlaceProtocol: current state, forecast, optimize caffeine/dosing schedule, circulating level, related outputs Inputs/outputs use absolute Dates only at the boundary Single builder path: sleep + dose events + resolved subject profile - engine input Temporary in-house adapter implements the same protocol (dual-run baseline) DI provider swaps implementations without UI rewrites Planned doses are a real flag on events (not free-text parsing) Personalization traits resolved app-side and passed in; integrator does not own trait fitting unless contract expands On-device only: no backend; computation and storage stay on device.ResponsibilitiesPackage licensed lab library (SPM and/or XCFramework); pin versions; document upgrade path Implement protocol-conforming adapter; map lab SDK I/O ? app DTOs Lock unit/timebase/dose policy (see existing unit-policy docs); touchscreen vs hardware RT policy where relevant Build dual-run / golden regression harness vs in-house baseline; no silent behavior drift Feature-flag provider swap; document failure modes (missing sleep, bad inputs, SDK errors) Own release risk for incorrect predictions at the engine boundary; science/product sign-off on parity Must-haveStrong Swift and iOS (async/await, actors / @MainActor, working inside an existing SwiftUI codebase) Wrapping third-party native libraries (XCFramework / SPM), binary vs source integration, module/linker issues Defining and locking I/O contracts (timebases, units, dose schedules, absolute timestamps) Test discipline: dual-run, golden/regression tests, CI-friendly harnesses; no silent drift ? Clear async written communication; Mac development environment Nice-to-haveSleep / circadian / caffeine PK-PD or biomathematical modeling exposure ? HealthKit, BackgroundTasks, local notification scheduling Scientific, clinical, or safety-critical app experience On-device performance profiling; numerical stability awareness Prior closed-source / licensed SDK integration under NDA