Bring machine learning models to phones and wearables to provide real-time health insights, including offline. Optimize and deploy models for on-device inference, coordinate local and cloud processing, and collaborate with mobile, cloud, data science, and platform teams. Validate performance and outputs while supporting privacy, regulated development, and technical documentation.
Responsibilities
- Convert, optimize, and deploy models to i
- OS, Android, and wearable devices.
- Apply quantization, pruning, and distillation to meet latency, memory, and battery targets.
- Design on-device and cloud processing, including offline behavior, fallback, and synchronization.
- Build versioned processes for updating models on devices.
- Validate device model outputs against reference models and test across devices and operating systems.
- Collect on-device performance and quality metrics, integrate models with apps, and contribute to verification and documentation.
Required Skills
- Core MLTensor
- Flow Lite
- ONNX Runtime
- Model quantization
- Model pruning
- Model distillation
- Python
- Swift
- Kotlin
- C++
