Mobile AI/ML Engineer

Title: Mobile AI/ML Engineer Location: Mountain View, CA Duration: 6 months, with potential extension Position Summary: We are looking for a highly capable Android AI/ML Engineer 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 LiteML Kit (including GenAI APIs)MediaPipePyTorch 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 (e.g., MCP or similar) for:Model versioning, delivery, and remote updatesTelemetry collection and model performance monitoringRollout and A/B testing infrastructureImplement 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.

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 analyticsExperience 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 (e.g., 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 (e.g., 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.5-7 years of experience with a Masters degree, 3+ years of experience with a PhD