Android AI ML Engineer - Infrastructure

Design, develop, and deploy on-device machine learning models optimized for Android, ensuring low latency and minimal resource consumption.

Build robust and scalable ML pipelines using Android-native frameworks such as:

Design, develop, and deploy on-device machine learning models optimized for Android, ensuring low latency and minimal resource consumption.

Build robust and scalable ML pipelines using Android-native frameworks such as:

TensorFlow Lite

ML Kit (including GenAI APIs)

MediaPipe

PyTorch Mobile

Implement local signal aggregation and real-time pattern recognition logic to enable responsive in-app actions driven by on-device inference.

Architect systems that support telemetry, secure logging, and privacy-first feedback collection for monitoring and evaluation.

Apply model compression and optimization techniques (e.g., quantization, pruning, distillation) to meet mobile performance constraints.

Develop secure, privacy-first solutions where all data processing and ML inference occur strictly on-device, with no external data exposure.

Enable mechanisms for continuous local learning and model updates using device-resident data and signals, without compromising privacy.

Ensure integration with Android's security model and collaborate with platform and product teams to deploy AI features safely at scale.

Skills: Technical Requirements:

Proven experience in Android development (Kotlin/Java), with strong understanding of system architecture, resource management, and performance tuning.

Hands-on expertise with on-device ML frameworks including TensorFlow Lite, ML Kit, MediaPipe, and PyTorch Mobile.

Solid foundation in machine learning and signal processing techniques, such as time-series modeling, clustering, classification, and real-time event detection.

Strong knowledge of mobile data handling and Android security practices, including permissions, sandboxing, and secure data storage.

Understanding of privacy-preserving learning techniques and data governance in mobile environments.

Familiarity with secure data handling on Android, including encrypted storage, permissions, sandboxing, and secure compute enclaves.

Experience with telemetry systems and evaluation pipelines for monitoring model performance on-device at scale.

Preferred Qualifications:

Experience building ML-driven mobile applications in domains requiring user personalization, privacy, or security.

Understanding of real-time data processing and behavioral modeling on resource-constrained edge devices.

Knowledge of on-device learning techniques, federated learning, or personalization methods.

Prior contributions to systems using federated learning, differential privacy, or local fine-tuning of models is a plus

Experience with backend infrastructure for model management (e.g., model registries, update orchestration, logging frameworks) is a plus.

Prior work with anomaly detection or behavioral modeling in resource-constrained environments is a plus.

Experience developing responsive systems capable of monitoring local context and dynamically triggering actions based on model outputs is a plus

Experience optimizing models for ARM architectures is a plus

Keywords:

Education:

5-7 years of experience with a Masters degree, 3+ years of experience with a PhD