Mobile & AI Engineer

Hi! I am Ryan

Full-stack engineer building production apps with React Native, Swift, computer vision, and privacy-first AI.

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Ryan Yang

Mobile & AI Engineer

01. BIO

Product-Focused Engineer.

I build production-minded mobile products that combine applied AI, native device capabilities, and careful UX. Across HabitForge, Seekr, and TrainTogether, plus this portfolio website, I have shipped work spanning React Native, Swift, Gemini-backed workflows, on-device models, LiDAR scanning, spatial search, and resilient frontend systems.

At the University of Toronto, I am strengthening my foundations in algorithms, math, and software design while continuing to ship real products. My volunteer work in tech support and teaching sharpened my communication and collaboration, and I care about building software that is technically rigorous, reliable, and genuinely useful.

0 Years Exp

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02. WORKS

Featured Projects

HabitForge

React Native Expo TypeScript Swift Gemini WidgetKit

- Structured the app around habit, finance, and rewards domains so the product can scale without feature coupling

- Built natural-language expense capture and budget workflows that reduce friction for repeated daily logging

- Layered Gemini-backed insights behind caching so recommendations stay responsive and cost-aware

- Added a defensive native widget bridge with a consolidated sync pipeline for habits, budgets, and expenses

- Combined gamification, finance visibility, and AI guidance into one cohesive consumer product loop

View on App Store Support

Seekr

Swift ARKit LiDAR RTAB-Map C++ Bridge Multipeer SceneKit

- Streams ARKit and LiDAR odometry through a Swift-to-C++ bridge into RTAB-Map for real-time mobile SLAM

- Uses hybrid persistence with CoreData metadata, OBJ meshes, RTAB databases, and disk cache for reliable restoration

- Maintains responsive mapping UX through native state management and throttled UI updates during heavy scans

- Adds hierarchy-aware tags and synonym-based retrieval so scanned spaces stay searchable after capture

- Shares spaces privately over MultipeerConnectivity without introducing a cloud dependency

View on App Store Support

TrainTogether

Swift SwiftUI QuickPose MediaPipe On-device LLMs SQLite HealthKit

- Runs a modular pose pipeline from landmark capture through smoothing, rolling averages, and biomechanical checks

- Uses FormConfig and RepFormTracker patterns so new exercises plug into a shared scoring and feedback framework

- Combines rule-based coaching with optional on-device LLM runtimes for privacy-conscious workout feedback

- Persists sessions through SQLite migrations with optional CloudKit flows instead of making the workout loop cloud-dependent

- Supports deterministic testing with mock landmarks so CV and rep logic can be validated beyond live camera demos

View on App Store Support

03. ENGINEERING

How I Build

Three pillars: polished mobile products, applied AI, and privacy-first architecture.

Mobile Product Engineering Pillar 01

Build polished mobile products

From interface systems to widgets, sync, exports, and device-native integrations.

React Native + Expo SwiftUI WidgetKit iCloud sync

Delivery focus End-to-end UX from screen flows to platform hooks.

Operating style Production-minded mobile systems that stay reliable beyond the demo.

AI, ML & Computer Vision Pillar 02

Apply AI where it helps

Cloud models, on-device inference, and classical vision pipelines chosen by product constraint.

Gemini llama.cpp CoreML RTAB-Map + ARKit

Product lens Latency, privacy, and workflow value decide the model strategy.

Proof On-device coaching in TrainTogether and spatial CV in Seekr.

Privacy-First Systems Pillar 03

Design around trust and offline utility

Local-first storage and narrow cloud usage so the product remains useful when privacy and connectivity matter.

Local-first storage Peer-to-peer sync Private iCloud container Offline-capable flows

Boundary setting Keep sensitive data close to the device whenever the product allows it.

Reliability Build workflows that still work under latency, weak signal, or strict privacy needs.

Website case study

This portfolio is also part of the proof: a static product with optimistic UI, local fallbacks, and Supabase-backed interactions that stays polished when infrastructure is imperfect.

Static hosting Optimistic UI Local fallback logic

04. VISUALS

Lens & Light

Full Gallery

05. CONTACT

Let's Talk.

ryan@ryanyang05.me