Android AI ML Engineer - On-Device

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Position Summary: We are looking for a highly capable Android AI/ML Engineer - On-Device to help build intelligent, privacy-first mobile systems that can detect, respond to, and learn from dynamic real-world conditions. This role involves deploying resource-efficient ML models directly on Android devices, combined with backend integration for model management, telemetry, and secure update delivery. The ideal candidate has a strong background in on-device intelligence and cloud-integrated systems, especially in applications that require responsiveness, adaptability, and strict privacy controls. 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 Build robust and efficient on-device data pipelines and inference mechanisms for real-time decision-making.
Apply model optimization techniques such as quantization, pruning, and distillation for performance on mobile hardware.
Ensure privacy-first design by performing all data processing and inference strictly on-device.
Collaborate with backend teams to integrate with cloud-based model orchestration systems (, MCP or similar) for: Model versioning, delivery, and remote updates Telemetry collection and model performance monitoring Rollout and A/B testing infrastructure
Implement secure local storage, encrypted data handling, and telemetry pipelines that meet privacy and compliance standards.
Support adaptive model behavior through on-device fine-tuning, personalization, or federated learning workflows. Skills: Technical Requirements: Proficiency in Android development using Kotlin and/or Java with deep understanding of app architecture, background processing, and system APIs.
Hands-on experience with on-device ML frameworks: TensorFlow Lite, ML Kit, MediaPipe, PyTorch Mobile.
Solid understanding of mobile performance optimization, including model size, memory usage, and latency.
Proven ability to integrate Android apps with backend/cloud systems for:
Model lifecycle management (delivery, updates, rollback)
Logging, telemetry, and analytics
Experience with secure Android development, including permissions, sandboxing, encryption, and local data protection.
Strong understanding of privacy-first ML system design and local-only data processing. Preferred Qualifications: Experience working with model orchestration platforms (, MCP, Vertex AI, SageMaker, or internal tools).
Familiarity with federated learning, on-device personalization, or differential privacy.
Background in building real-time, data-driven features in mobile apps at scale.
Familiarity with cloud infrastructure (, GCP, AWS) for ML model deployment and monitoring.
Previous work in high-sensitivity domains such as identity, privacy, mobile security, or regulated industries is a plus.
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Location:
Mountain View

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