Senior Mobile Engineer – iOS/Android, Realtime AI
这是一个远程职位。我们正在聘请一名高级工程师来构建与 RayBan Meta 智能眼镜的 Metas Glasses SDK 集成的生产移动应用程序和服务。你将设计并发送免费手，语音先，视觉启用体验，覆盖眼镜，伴侣 iOS/Android 应用程序和云 AI.这是移动，实时音频/视频，蓝牙/WiFi 运输和多模式人工智能的交叉点的一个高度跨功能角色。你将做什么？拥有通过 MetaS Glasses SDk 接口的伴侣移动应用的端到端开发 · 实现可靠的捕获和流媒体管道，用于摄像头预览框架，静态图片和多道音频，严格关注延迟，电池和隐私指标 （摄像头 LED,权限）·建立语音首次使用：唤醒字传递，推到对话流程，VAD/ASR/TTS,耳机以及低延迟音频播放在开放式扬声器上 ·集成手机计算机视觉和语音模型 (Core ML,MediaPipe,ONNX Runtime,NAPI) 并通过运输 APP 进行云计算演示。蓝牙 LE 控制道，WiFi/WiFi 直播媒体流量，连接逻辑和设备对配合和会议生命周期的状态机 ·设计具有阻力，可观测的管道，反压力，重试，离线倒退以及温度，带宽或权限变化的优雅降解 ·与产品/设计合作在语音首次交互模式上；运行用户测试；延迟，准确性和任务完成仪器指标 ·建立移动 CI/CD,自动化测试 （单元/集成/BT 装置循环中）,崩盘/错误分析和发布过程 ·通过设计和遵守平台和旁观者安全政策来获得冠军 最低资格 · 5年以上的专业移动工程经验，在 iOS 或 Android （最好是两者）上运送本土应用程序；•深度熟练掌握：· iOS:Swift, SwiftUI/UIKit,AVFoundation,CoreBluetooth, CoreMotion,背景模式，同步性 (GCD/AsyncAwait),音频单元 · Android: Kotlin, Jetpack Compose/Views, Bluetooth/BLE,相机/媒体，前景服务，Coroutines/Flow ·实时媒体管道和流程：音频捕获/播放，回声取消，噪声抑制，唇色/延迟预算，Websockets/gRPC/WebRTC,编码器 (Opus/AAC/PCM) ·固体网络系统和系统
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This is a remote position.
We’re hiring a Senior Engineer to build production mobile apps and services that integrate with Meta’s Glasses SDK for RayBan Meta smart glasses. You’ll design and ship hands-free, voice-first, vision-enabled experiences that span the glasses, a companion iOS/Android app, and cloud AI. This is a highly cross-functional role at the intersection of mobile, real-time audio/video, Bluetooth/WiFi transport, and multimodal AI.
What you’ll do
· Own end-to-end development of a companion mobile app that interfaces with RayBan Meta smart glasses via Meta’s Glasses SDK
· Implement reliable capture and streaming pipelines for camera preview frames, stills, and multichannel audio, with strict attention to latency, battery, and privacy indicators (camera LED, permissions)
· Build voice-first UX: wakeword handoff, push-to-talk flows, VAD/ASR/TTS, earcons, and low-latency audio playback on openear speakers
· Integrate on-phone computer vision and speech models (Core ML, MediaPipe, ONNX Runtime, NNAPI) and orchestrate cloud inference for multimodal LLMs (e.g., Llama 3family vision/voice) via streaming APIs
· Handle transport and connectivity: Bluetooth LE control channels, WiFi/WiFi Direct media streaming, reconnect logic, and state machines for device pairing and session lifecycles
· Design resilient, observable pipelines with backpressure, retries, offline fallbacks, and graceful degradation when thermals, bandwidth, or permissions change
· Collaborate with product/design on voice-first interaction patterns; run user tests; instrument metrics for latency, accuracy, and task completion
· Establish mobile CI/CD, automated testing (unit/integration/BT device in the loop), crash/error analytics, and release processes
· Champion privacy by design and compliance with platform and bystander safety policies
Requirements
Minimum qualifications
· 5+ years professional mobile engineering experience, shipping native apps at scale on iOS or Android (preferably both)
- Deep proficiency in:
- iOS: Swift, SwiftUI/UIKit, AVFoundation, CoreBluetooth, CoreMotion, background modes, concurrency (GCD/AsyncAwait), audio units
- Android: Kotlin, Jetpack Compose/Views, Bluetooth/BLE, Camera/Media, Foreground services, Coroutines/Flow
- Handson with realtime media pipelines and streaming: audio capture/playback, echo cancellation, noise suppression, lipsync/latency budgeting, WebSockets/gRPC/WebRTC, codecs (Opus/AAC/PCM)
- Solid networking and systems skills: state machines, threading, buffering, backpressure, power/thermal profiling, and debugging on constrained devices
- Experience integrating cloud AI services (LLM/ASR/TTS) and handling streaming inference results in the UI
- Strong product sense for voice-first UX and accessibility in eyes-up, hands-free contexts
- Excellent communication; comfortable working with early/preview SDKs and ambiguous requirements
- Preferred qualifications
- Prior work with Meta’s Glasses SDK (RayBan Meta) or similar wearables (Apple Watch/visionOS audio, Snap Spectacles, Bose Frames) – if you don’t have this experience, it is fine, provided that you are willing to learn the Meta Glasses SDK quickly
- Ondevice ML on phone: Core ML/Metal/Accelerate (iOS), MediaPipe/TFLite/NNAPI/GPU delegates (Android), ONNX Runtime Mobile
- Multimodal AI integration: experience with Llamafamily models, Whisper/Seamless/other ASR, TTS providers, prompt and latency optimization, partial results/streaming UX
- BLE expertise: GATT design, connection strategies, MTU/throughput tuning, coexistence with WiFi transport, device provisioning, and firmware update flows
- WebRTC for low-latency A/V; adaptive bitrate, jitter buffers, AEC/VAD tuning
- Backend exposure sufficient to move fast with AI
- Python or Node.js for inference gateways (FastAPI/Express), WebSocket servers, request fanout, and token streaming
- Deploying model servers (Triton, vLLM) and vector/RAG stacks (FAISS/Pinecone), observability (OpenTelemetry), and autoscaling on AWS/GCP/Azure
- Security and privacy: keychain/keystore, secure BLE pairing, PII handling, consent UX, regional data controls
- QA for hardwareintheloop: writing automated tests that exercise glasses events (connect, capture, LED state, battery, IMU), and performance tests for end-to-end latency
Originally posted on Himalayas