Android AI/ML Engineer - Infrastructure

37 Days Old

Job Description

Job Description

Android AI/ML Engineer (Enterprise & Security Innovation Lab)
We are looking for an experienced Android AI/ML Engineer 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
Compensation: $70 - $93.19 per hour
ID#: 36130169
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Location:
Mountain View
Category:
Technology

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