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People · Technology
Staff Mobile Engineer specializing in iOS, React Native (Expo), full-stack and Android development, software architecture, AI agents, LLMs, backend services, and security. Hands-on engineer delivering products at scale.
Staff Mobile Engineer · Trustworthy
Rob S. — Staff Mobile Engineer · Trustworthy · Canada.
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About
https://robsandhu.com
Experience
Staff Mobile EngineerCurrent
Trustworthy · San Francisco Bay Area
Jan 2021 – Present
Lead, own and architect the mobile app — built on Expo & React Native — from requirements through production. Applying agentic development tools such as Codex and Claude to design, implement and test production level codebases. Designed a local-first architecture: user data fully available offline, encrypted at rest, and synced when online. Implemented an encrypted on-device SQLite store (Expo SQLite) and on-device file encryption via Quick Crypto for React Native. Built the mobile sync engine end-to-end, partnering with the CTO on API design and conflict-resolution mechanisms. Authored native modules in Swift and Kotlin where the JS layer wasn’t enough — bridging platform APIs into RN. Owned CI/build infrastructure, E2E integration tests, and OTA delivery via EAS Update (previously CodePush). Active in product decisions and direction; take features from spec to documentation to shipped in a fast-paced environment. Mentor engineers across the mobile team on RN architecture and shipping discipline. Trustworthy is an AI-native platform for families. Built CI/CD with GitHub Actions, EAS Build, and Fastlane. Ship multiple weekly releases and OTA updates; own Maestro and XCTest testing, Sentry and Datadog monitoring, and incident response. Mentor engineers, set code-review and architecture standards, and lead planning and roadmap discussions with product, design, and leadership.
Senior Software Engineer
Camino · San Francisco Bay Area
Jan 2018 – Oct 2019
Fast-paced startup environment — involved in both product direction and technical architecture decisions. Owned the iOS app (Swift), rendering personalized commute feeds of music, podcasts, and news tuned to the listener’s context. Built the recommendation engine in TensorFlow (Python) on Django, modeling user taste from listening history and time-of-day signals. Designed and shipped backend services and APIs to capture playback events — the training-data pipeline feeding the recommender. Worked across the full vertical: client → API → model → back to client, closing the loop between user behavior and recommendations.