Job-ID Reference Salary$123.46 / hourRemote50% RemoteSenior Swift Engineer - AI Platform
About the Role
We are building a native application that delivers AI-powered data insights for business users. This is a systems-level contract engagement — you'll work across AI runtime, platform integrations, deterministic data execution, or adaptive UI depending on your strengths and interests. The architecture is multi-stage pipeline driven, with strict layered dependencies across 20 Swift Package Manager targets and no shortcuts. The application is a structured, multi-stage pipeline that turns natural language into verified, traceable results. We need engineers who ship real desktop software and take ownership of their technical surface.
What You'll Do Own one or more technical surfaces within a native AI pipeline: runtime/orchestration, platform/integrations, deterministic execution, or adaptive UI. Design and implement production Swift code within a layered SPM architecture (20 targets) with strict dependency boundaries from foundation types through orchestration to application layer. Build AI orchestration including structured output extraction, multi-stage routing, tool calling, context management, and response synthesis. Implement platform integrations — protocol-based connector clients, enterprise data access, entitlements, sandboxing, identity, and persistence boundaries. Build deterministic execution pathways — joins, aggregations, rankings, filters — with full data provenance and traceability. Develop adaptive UI using SwiftUI with AppKit interop — desktop windowing, keyboard-first workflows, streaming content updates, state management, and accessibility. Write and maintain automated tests using XCTest and Swift Testing frameworks. Ensure all shared mutable state uses actors or Sendable value types with correct isolation annotations under Swift 6 strict concurrency. Required Experience 5 years of professional software engineering experience with Swift Experience shipping desktop software (not only) Strong understanding of Swift concurrency: actors, async/await, Sendable, structured concurrency, and typed throws Experience working within layered, modular SPM architectures with strict dependency management Proficiency with protocol-oriented design and dependency injection patterns Comfortable with pre-commit hooks, strict linting (SwiftLint, SwiftFormat), and enforced commit conventions Preferred Experience (one or more of the following)
AI Runtime and Orchestration - LLM integration: guided generation, structured output, tool calling, streaming inference - Experience with ML inference frameworks and model orchestration - Token budget management, prompt composition, multistage pipeline orchestration - Evaluation and benchmarking of non-deterministic AI outputs
Platform and Integrations - Protocol-based connector/client architectures (SSE, stdio, REST, OData) - system services: process lifecycle management (launchd, SMJobBless), entitlements, sandboxing - Code signing, notarization, and installer packaging for distribution - Enterprise data access patterns and pagination strategies
Deterministic Execution and Data Systems - TabularData or equivalent data frame processing in Swift - Joins, aggregations, rankings, filters with provenance tracking - Graph or vector retrieval systems, entity resolution - Schema-driven field matching and query planning
Adaptive UI - Production SwiftUI applications with AppKit interop on - Adaptive layouts, multi-panel desktop windowing, keyboard-first navigation - Streaming UI updates tied to async data pipelines - Accessibility compliance and performance optimization (Instruments profiling)
Technical Environment Swift 6.2, 26 , SPM-only (no Xcode project file) 20 SPM targets across foundation, stage, orchestration, and application layers LLM integration and ML inference frameworks for AI-powered features XCTest and Swift Testing for test automation SwiftLint SwiftFormat with strict pre-commit enforcement Git-based workflow with conventional commits and CI pipeline integration
About the Role
We are building a native application that delivers AI-powered data insights for business users. This is a systems-level contract engagement — you'll work across AI runtime, platform integrations, deterministic data execution, or adaptive UI depending on your strengths and interests. The architecture is multi-stage pipeline driven, with strict layered dependencies across 20 Swift Package Manager targets and no shortcuts. The application is a structured, multi-stage pipeline that turns natural language into verified, traceable results. We need engineers who ship real desktop software and take ownership of their technical surface.
What You'll Do Own one or more technical surfaces within a native AI pipeline: runtime/orchestration, platform/integrations, deterministic execution, or adaptive UI. Design and implement production Swift code within a layered SPM architecture (20 targets) with strict dependency boundaries from foundation types through orchestration to application layer. Build AI orchestration including structured output extraction, multi-stage routing, tool calling, context management, and response synthesis. Implement platform integrations — protocol-based connector clients, enterprise data access, entitlements, sandboxing, identity, and persistence boundaries. Build deterministic execution pathways — joins, aggregations, rankings, filters — with full data provenance and traceability. Develop adaptive UI using SwiftUI with AppKit interop — desktop windowing, keyboard-first workflows, streaming content updates, state management, and accessibility. Write and maintain automated tests using XCTest and Swift Testing frameworks. Ensure all shared mutable state uses actors or Sendable value types with correct isolation annotations under Swift 6 strict concurrency. Required Experience 5 years of professional software engineering experience with Swift Experience shipping desktop software (not only) Strong understanding of Swift concurrency: actors, async/await, Sendable, structured concurrency, and typed throws Experience working within layered, modular SPM architectures with strict dependency management Proficiency with protocol-oriented design and dependency injection patterns Comfortable with pre-commit hooks, strict linting (SwiftLint, SwiftFormat), and enforced commit conventions Preferred Experience (one or more of the following)
AI Runtime and Orchestration - LLM integration: guided generation, structured output, tool calling, streaming inference - Experience with ML inference frameworks and model orchestration - Token budget management, prompt composition, multistage pipeline orchestration - Evaluation and benchmarking of non-deterministic AI outputs
Platform and Integrations - Protocol-based connector/client architectures (SSE, stdio, REST, OData) - system services: process lifecycle management (launchd, SMJobBless), entitlements, sandboxing - Code signing, notarization, and installer packaging for distribution - Enterprise data access patterns and pagination strategies
Deterministic Execution and Data Systems - TabularData or equivalent data frame processing in Swift - Joins, aggregations, rankings, filters with provenance tracking - Graph or vector retrieval systems, entity resolution - Schema-driven field matching and query planning
Adaptive UI - Production SwiftUI applications with AppKit interop on - Adaptive layouts, multi-panel desktop windowing, keyboard-first navigation - Streaming UI updates tied to async data pipelines - Accessibility compliance and performance optimization (Instruments profiling)
Technical Environment Swift 6.2, 26 , SPM-only (no Xcode project file) 20 SPM targets across foundation, stage, orchestration, and application layers LLM integration and ML inference frameworks for AI-powered features XCTest and Swift Testing for test automation SwiftLint SwiftFormat with strict pre-commit enforcement Git-based workflow with conventional commits and CI pipeline integration
Senior Swift Engineer - macOS AI Platform in sunnyvale at Unknown Company
This position is listed as contract and able to be worked remotely.