Android Application Engineer

LILTIndianapolis, IN

About The Position

LILT is looking for an Android Application Engineer to join the team building LILT Converse, our on-device instant translation application. Converse enables secure, real-time speech translation without an internet connection — making it the go-to solution for government, defense, and enterprise teams operating in communication-sensitive or connectivity-constrained environments. You will work at the intersection of cutting-edge on-device AI and polished mobile UX, building software that runs large multilingual speech models directly on Android hardware leveraging Qualcomm Snapdragon AI acceleration. This is a high-impact role on a product that is moving quickly. As the Android engineer on Converse, you will own the Android application end-to-end — from architecture and on-device ML integration through performance tuning, release, and reliability in the field — working closely with ML, product, and design on a small senior team. You will architect new features, integrate on-device ML models, optimize performance, manage large local model assets, and ensure rock-solid reliability in the field. You will ship capabilities like multilateral translation (simultaneous 3+ language conversations), utterance management, kiosk deployment modes, and enterprise configuration. You will be comfortable reasoning about hardware constraints, APK packaging, sideloading workflows, and the tradeoffs of running multi-gigabyte inference workloads on mobile SoCs. We are looking for someone who thrives in a fast-moving startup environment and takes pride in shipping software that works in the real world under demanding conditions.

Requirements

  • 4+ years of professional Android development (Kotlin), with a track record of owning a production Android app end-to-end — architecture, implementation, release, and field reliability
  • Strong Android fundamentals: Android SDK, Jetpack, lifecycle, background work, and storage APIs
  • Experience integrating native libraries via NDK/JNI (ML runtimes, audio engines, or similar)
  • Comfort optimizing performance on physical devices — memory, latency, thermal behavior under sustained workloads
  • Experience with offline-first applications and large on-device assets (multi-GB model files, packaging, sideloading)
  • Real-time audio on Android: microphone capture, TTS/speaker output, and low-latency audio pipelines (including USB microphone support)
  • On-device speech pipeline integration: wiring ASR → translation → TTS, handling streaming partial results, and managing turn-taking in a live conversation UI
  • Enterprise deployment familiarity: sideloading, MDM/kiosk modes, pre-provisioned device configuration
  • Bias toward shipping reliable software in demanding, real-world conditions

Nice To Haves

  • On-device ML runtime integration (ONNX Runtime, TensorFlow Lite, or Qualcomm QNN / Snapdragon NPU acceleration)
  • External display / kiosk UX (HDMI output, presentation layers, broadcast-style overlays)
  • Secure data handling: encrypted export, USB storage workflows, air-gapped or connectivity-constrained environments
  • Prior work with government, defense, or enterprise customers
  • Deep speech/audio domain expertise beyond app integration — e.g. tuning latency/quality tradeoffs, noise handling and audio preprocessing, model quantization on mobile SoCs, or direct work with speech model runtimes
  • iOS / Swift experience
  • CI/CD with hardware-in-the-loop testing on physical devices

Responsibilities

  • Architect new features
  • Integrate on-device ML models
  • Optimize performance
  • Manage large local model assets
  • Ensure rock-solid reliability in the field
  • Ship capabilities like multilateral translation (simultaneous 3+ language conversations), utterance management, kiosk deployment modes, and enterprise configuration
  • Reason about hardware constraints, APK packaging, sideloading workflows, and the tradeoffs of running multi-gigabyte inference workloads on mobile SoCs
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