Job Title: AI Architecture Lead  macOS Forensics

Company Name: SUMURI

Job Details: RemoteFull,Time

Job Url: https://hiring.cafe/viewjob/qmkx4zi4b44ky1ki

Job Description: Posted 1mo agoAI Architecture Lead  macOS Forensics@ SUMURIView All JobsWebsiteMagnolia, Delaware, United StatesRemoteFull TimeResponsibilities:design architecture, lead team, coordinate securityRequirements Summary:7+ years software engineering; 3+ years Swift; macOS native apps; ML model integration; Core ML/ONNX; Apple Silicon; security; leadership.Technical Tools Mentioned:Swift, SwiftUI, AppKit, Core ML, Metal, PyTorch, ONNX Runtime, Vision framework
JOB DESCRIPTIONAI Architecture Lead – macOS ForensicsLocation: Remote (US Preferred)Company: SUMURI LLC – Magnolia, DelawareReports To: Founder / Director of SoftwareProduct Focus: RECON ITR & RECON LAB (macOS-native forensic tools)About SUMURISUMURI is a Delaware-based digital forensics company specializing in macOS forensicsoftware and hardware used by law enforcement, military, and corporate investigatorsworldwide. Our flagship tools — RECON ITR (imaging & triage) and RECON LAB (analysis &reporting) — are undergoing a modern Swift-native rebuild designed for Apple Silicon andlong-term AI integration.We are building the most advanced macOS forensic AI platform in the world.Position SummaryThe AI Architecture Lead will design and oversee the long-term AI and ML architecture forRECON ITR and RECON LAB, ensuring:● Native Swift/macOS integration● Apple Silicon optimization● Offline AI model execution● Forensic defensibility● Scalable feature velocity using AI coding agents● Strict privacy and security standardsThis is not a web AI role.This is not a prompt-engineering role.This is a macOS-native forensic AI systems architecture role.Core Responsibilities1. AI Architecture Strategy● Design a long-term AI integration roadmap for RECON LAB and RECON ITR● Architect modular AI pipelines (OCR, face detection, object detection, CLIP-stylelabeling)● Define standards for pretrained model integration (no custom model training requiredinitially)● Ensure deterministic, explainable AI workflows suitable for court testimony2. macOS & Swift Integration● Architect AI features using:○ Swift○ SwiftUI / AppKit○ Core ML○ Metal (if needed)● Optimize for Apple Silicon (M-series)● Convert PyTorch / ONNX models into Core ML where appropriate● Ensure compatibility with macOS notarization and sandboxing requirements3. AI Coding Agent Management● Design workflows for:○ Using LLM coding agents safely○ Automated code validation pipelines○ Preventing hallucinated unsafe logic○ Enforcing architectural consistency● Build structured AI-assisted development pipelines● Implement guardrails for secure code generation4. Forensic Integrity & Defensibility● Ensure:○ AI outputs are logged and reproducible○ Chain of custody is preserved○ Processing is transparent and reviewable○ No cloud dependency unless explicitly configured● Design AI workflows that withstand Daubert/Frye scrutiny5. Performance & Security● Architect offline-first inference pipelines● Ensure no unintended data exfiltration● Implement sandboxed model execution● Optimize inference performance for:○ 16GB, 32GB, 64GB Apple Silicon systems● Reduce memory overhead in large case processing6. Leadership● Lead small AI engineering team● Review Swift and ML code for production quality● Mentor developers transitioning from C++/QT to Swift● Collaborate with external development partners● Set coding standards and documentation requirementsRequired QualificationsTechnical● 7+ years professional software engineering experience● 3+ years production Swift development● Deep experience building macOS native applications● Experience integrating ML models into native applications● Experience converting models (PyTorch / ONNX → Core ML)● Strong understanding of:○ Apple Silicon architecture○ Memory optimization○ Concurrency (GCD, async/await)○ Security best practices● Experience managing large codebasesAI / ML Experience● Experience implementing:○ Object detection (YOLO-style)○ OCR pipelines○ Face detection & embedding comparison○ CLIP-style zero-shot classification● Experience deploying pretrained models (not necessarily training them)● Familiarity with:○ Core ML○ ONNX Runtime○ PyTorch○ Vision framework● Understanding of deterministic vs probabilistic outputsForensic or High-Security Environment Experience (Preferred)● Experience in digital forensics● Experience in cybersecurity● Experience building tools used in regulated environments● Understanding of evidentiary handling principlesNice-to-Have (But Not Required)● Experience testifying or supporting expert testimony● Experience building offline AI systems● C++ interoperability knowledge● Metal acceleration knowledge● Experience building CLI forensic tools● Experience with APFS / macOS internalsWhat Success Looks Like (12–24 Months)● RECON LAB has modular AI engine framework● All AI runs offline by default● AI coding agents reduce feature development time by 40%+● No AI-related architectural rewrites required● Clean Swift-native codebase● Clear AI audit logging system● Production-ready model update pipeline● Competitive advantage over SaaS-only forensic vendorsCompensationCompetitive, based on experience.Equity discussion possible for exceptional candidates.