Apple AI/ML Engineer / Foundation Models Engineer
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- Build Apple-native foundation-model experiences with Swift, privacy and the right execution path
- Engineer agentic workflows, local/server orchestration and evaluation-driven feature quality
- Optimize Apple AI for on-device scale, distributed execution and production diagnostics
Benchmark: Apple Foundation Model Services / Inference + current AIML foundation-model, Core AI and evaluation engineering signals (current Sep 2026). Market refresh: Foundation Models framework; on-device + Private Cloud Compute model paths; LanguageModel protocol; Dynamic Profiles and tools; Core AI on Apple silicon; MLX local/distributed inference and training; Evaluations framework and model judges; Instruments; privacy, low latency and production-scale serving.
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Build Apple-native foundation-model experiences with Swift, privacy and the right execution path
Foundation Models framework + model-selection architecture
Coverage: Swift / FoundationModels / LanguageModel Protocol / Apple Foundation Models / PCC / Third-Party Models / Structured Output
On-device deployment + Apple silicon performance foundations
Coverage: Core AI / Apple Silicon / CPU-GPU-Neural Engine / Model Conversion / Swift Inference / Memory / Ahead-of-Time Compilation
Engineer agentic workflows, local/server orchestration and evaluation-driven feature quality
Agentic context, tools + local/server orchestration
Coverage: Dynamic Profiles / Tool Calling / Context Management / Caching / Model Handoffs / Private Cloud Compute / Privacy Boundaries
Evaluation-driven development + agent-behavior testing
Coverage: Evaluations Framework / Model Judges / Synthetic Data / Tool-Call Trajectories / Datasets / Regression / Quality Gates
Optimize Apple AI for on-device scale, distributed execution and production diagnostics
Core AI optimization, profiling + custom model engineering
Coverage: Core AI Optimization / PyTorch Extensions / Metal Kernels / Instruments / Compilation / Specialization / Memory / Debugging
MLX local agents + distributed inference and training
Coverage: MLX / MLX-LM / Local Agents / Local Serving / Distributed Inference / Training / Multi-Mac Scaling / Apple Silicon
Role-Readiness Proof / Capstone
Build and ship an Apple-platform AI feature that works across model tiers while preserving privacy and measurable quality. Implement the experience in Swift with the Foundation Models framework and typed structured output; use the on-device Apple Foundation Model by default, add tools and Dynamic Profiles for a multi-step workflow, and define a graceful Private Cloud Compute or provider-model handoff when local capability is insufficient. Integrate one custom or open model through Core AI or MLX, profile latency and memory and optimize it for target Apple silicon; create an Evaluations suite with representative and synthetic cases, model-judge metrics and tool-trajectory checks; diagnose failures with Instruments; enforce data minimization, authentication and action-approval boundaries; compare quality, latency, privacy and resource tradeoffs across execution paths; then stage the release and deliver a production-readiness package, runbook and measurable user-impact evidence.
Proof Rubric
- Prove correct Foundation Models and LanguageModel architecture
- disciplined on-device-versus-PCC/provider selection
- reliable structured output and tool behavior
- privacy-preserving context and data flow
- strong Core AI or MLX model integration and performance engineering
- representative eval datasets and trustworthy judges
- agent trajectory and regression coverage
- actionable Instruments traces
- graceful fallback and error handling
- latency, memory and resource awareness
- secure action boundaries
- release and rollback readiness
- and measurable user value with explicit tradeoffs among privacy, quality, speed, capability and operating cost