Staff Engineer - Mobile, Desktop & KMP
On-siteBengaluru, Karnataka, India
Job Summary
Own the end-to-end architecture of Sarvam's mobile and desktop applications using Kotlin Multiplatform to unify code across Android, iOS, Windows, Linux, and macOS. Ship and evolve Indus and Kivi as native apps handling chat, voice capture, streaming model output, and offline-first behavior while integrating ASR, TTS, and LLM APIs. Lead the KMP adoption strategy by defining shared modules for networking, persistence, and audio pipelines, and set engineering standards for CI/CD, automated testing, and code review culture. Drive performance optimization for cold-start times, memory footprint, and audio latency on low-RAM devices and patchy networks. Mentor engineers on platform-native depth and KMP discipline, partnering with backend and product teams to shape API contracts and deliver coherent native experiences.
Required Qualifications
- 9+ years building production mobile and/or desktop applications that real users depend on, with strong ownership of what you shipped
- Deep Kotlin expertise in production, including coroutines, flows, and modern Android architecture
- comfort with Swift on iOS and with at least one desktop platform (Windows, Linux, or macOS)
- Hands-on experience with Kotlin Multiplatform (KMP) on a shipped or in-production product designing shared modules, managing expect/actual boundaries, and dealing with the real friction of sharing code across mobile and desktop
- Strong command of native audio and streaming pipelines: audio capture, playback, buffering, VAD, codec handling, and low-latency streaming to and from model endpoints
- Experience shipping apps that work in constrained environments — offline-first, low-bandwidth, low-RAM — not just on a flagship device over fibre
- Solid networking and API integration instincts: streaming (WebSocket, SSE), retry and backoff, caching, optimistic updates, and debugging across the client-server boundary
- Hands-on experience with Compose Multiplatform and Jetpack Compose in production, and the judgment to drop to native UI frameworks and native libraries (SwiftUI on iOS, Win32 or GTK on desktop, platform audio or media APIs) when a shared abstraction would cost performance, correctness, or platform feel
- Familiarity with CI/CD and automated testing across multiple platforms, including real-device testing rather than emulator-only
- The judgment of a senior IC: when to share code vs. go native, when to abstract vs. inline, when to optimize vs. ship, and how to bring the rest of the team up with you
Desired Qualifications
- Experience with on-device model inference or on-device ML (TensorFlow Lite, Core ML, GGML, or equivalent) on mobile or desktop
- Background in speech or audio processing — codec work, sample-rate handling, echo cancellation, or VAD — in a production app
- Experience building voice-first or dictation products, especially multi-language or Indic language voice experiences
- Desktop app shipping experience via Kotlin/Native or JVM-based desktop targets, or prior work distributing apps through the Mac App Store, Microsoft Store, or Linux package managers
- Time at an early- or growth-stage startup: comfort with ambiguity, speed, and ownership
- Experience with Compose Multiplatform shipping to both mobile and desktop from a shared codebase
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