Android AI ML Engineer - Infrastructure

We are looking for an experienced Android AI/ML Engineer - Infrastructure to develop advanced on-device machine learning systems that enable secure, adaptive, and scalable intelligence across mobile devices. The role emphasizes building intelligent, adaptive, and privacy-preserving ML systems that operate efficiently within the constraints of mobile environments. The ideal candidate will have strong experience in designing real-time, context-aware inference systems that can respond dynamically to local data patterns and behaviors.

Key Responsibilities:

• 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.

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