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. WHAT WE'RE LOOKING FOR Required • 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 Strong plus • 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 Bonus • 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