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Read AI is building an AI-powered collaboration platform that brings intelligence to meetings, messages, and email. The company is backed by $81M in funding and is growing rapidly, adding over 1M new customers monthly.
You'll lead the evolution of Read's desktop application from a meeting capture tool into a unified control center where users prepare, capture, and generate outcomes from meetings. The app is built in Rust with Tauri on the frontend (React/TypeScript), with the most complex challenges at the OS boundary: system audio capture across Windows and macOS with different permission models, on-device processing, hardware integration, and release management for installed software.
Key responsibilities include shipping features that unify the desktop experience across prepare-capture-review workflows, expanding meeting capture to both Windows and macOS as first-class platforms, investigating on-device transcription and audio processing, connecting external devices to improve capture quality, and raising code quality standards with clear Rust patterns and comprehensive testing. You'll own the full release pipeline including signing, notarization, auto-updates, and crash telemetry.
You'll work directly with product and design, bringing technical vision to roadmap conversations and showing what's uniquely possible on desktop that web and mobile cannot achieve. Part of your role is R&D-oriented: prototyping new capabilities and determining technical feasibility.
Required: 5+ years professional software development with meaningful desktop/client application experience; shipped installed software to real users on Windows, macOS, or both; working proficiency in Rust or deep systems language experience with genuine appetite for Rust; comfortable across native/web boundaries (React, TypeScript); practical OS-level knowledge (permissions, device access, background processes, packaging, distribution); US work authorization (no visa sponsorship).
Nice-to-have: audio capture, DSP, real-time media pipeline experience; Tauri or comparable hybrid frameworks; on-device ML or local inference; hardware/peripheral integration.
