Location:

Mountain View, CA (On-site preferred, at least 4 days/week)

Position Summary:

We are looking for a highly capable engineer to design and deploy real-time, on-device machine learning solutions for mobile devices. This role focuses on developing privacy-first, resource-optimized ML systems that operate directly on Android hardware, supporting high-impact AI applications in real-world environments.

The ideal candidate brings deep technical expertise in on-device intelligence and mobile ML pipelines, and thrives in fast-paced environments that demand performance, security, and adaptability.

Key Responsibilities: Design and deploy efficient on-device ML models tailored for Android platforms. Build end-to-end ML pipelines using: TensorFlow Lite ML Kit (including GenAI APIs) MediaPipe PyTorch Mobile

Optimize models using techniques like quantization, pruning, and distillation to meet mobile performance targets. Develop context-aware, real-time inference systems and data pipelines on-device. Implement privacy-first architecture, ensuring all data processing and inference is local. Collaborate with backend/cloud teams to integrate model orchestration systems (e.g., MCP, Vertex AI, SageMaker) for: Model delivery and remote updates Telemetry and performance monitoring A/B testing and rollout strategies

Implement secure storage and encrypted data handling in line with privacy and compliance standards. Support adaptive model behavior using on-device personalization, federated learning, or similar privacy-preserving techniques. Technical Requirements:

Must-Have Skills: Strong Android development experience using Kotlin and/or Java Proficiency with on-device ML tools: TensorFlow Lite ML Kit MediaPipe PyTorch Mobile

Solid understanding of mobile constraints: Real-time inference Low-latency processing Model size and resource optimization

Experience in integrating mobile apps with backend/cloud systems for: Model lifecycle management Secure telemetry and data analytics

Knowledge of Android security best practices, including sandboxing, permissions, encryption, and local data protection Nice-to-Have Skills: Experience with federated learning, differential privacy, or on-device personalization Familiarity with cloud infrastructure (e.g., AWS, GCP) and ML deployment workflows Background in mobile AI features like anomaly detection, behavioral modeling, or privacy-focused applications Experience with model orchestration platforms such as MCP, Vertex AI, or SageMaker Education & Experience: Master's degree with 5-7 years of relevant experience, or PhD with 3 years of relevant experience Preferred: Less than 10 years of total professional experience Work Schedule: On-site presence preferred - at least 4 days/week in Mountain View, CA Standard business hours, minimal overtime except during key sprints Interview Process: 1 Technical Phone Screen 2 Virtual Technical Interviews Position Type & Growth Potential: Contract role with high potential for extension Strong possibility of full-time conversion based on performance and business needs Core Technical Keywords (for resume alignment): TensorFlow Lite (TFLite) ML Kit MediaPipe PyTorch Mobile On-device machine learning Mobile ML pipeline Edge AI Model quantization / pruning / distillation Real-time inference Federated learning Differential privacy Telemetry integration Secure Android development Model orchestration Cloud-integrated ML (e.g., Vertex AI, SageMaker, MCP)