Android AI ML Engineer - Infrastructure

FocusKPI is looking for an Android AI ML Engineer - Infrastructure to join one of our clients, a high-tech SaaS company.

The client is seeking 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.

Work Location: Mountain View, CA (on-site 4 days a week)
Duration: 6-month contract; hybrid role
Pay Range: 80/hr to $93/hr

**No C2C resumes are considered**

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.

Qualifications:

• 5-7 years of experience with a Master's degree, 3+ years of experience with a PhD
• Experience in fine tuning the model for mobile, must have an internal understanding of how it works (not only having a process level understanding). 
• Understanding how the models work from the ground up and be able to do it hands on from scratch and not just using a tool that integrates.
• A candidate with a strong on-device ML experience is highly prioritized and required
• A 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.
• Strong experience with model optimization and backend integration
• 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.
• Experience in Android development (Kotlin/Java) is good to have

Desired 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 are 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

**No C2C resumes are considered, and avoid Android developers for this role. **

Thank you!

FocusKPI Hiring Team

Founded in 2010, FocusKPI, Inc. (FocusKPI) is a data science and technology firm specializing in predictive analytics practice and methodologies. FocusKPI is a US company headquartered in Silicon Valley, California, with an East Coast office in Boston, Massachusetts.

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