Android AI ML Engineer - On-Device
Mountain View, CA
Contract Senior-level / Expert USD 186K+
FocusKPI
FocusKPI offers custom marketing analytics solutions and specialized data science support to optimize ROI and improve productivity. Among our areas of expertise are: personalization AI/ML solutions, measurement (MMM & MTA), customer sentiment...The client is 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.
Work Location: Mountain View, CA (on-site 4 days a week)
Duration: 6-month contract; hybrid role
Pay Range: 80/hr to $93/hr
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 (e.g., 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.
Qualifications:
- 5-7 years of experience with a Masters degree, 3+ years of experience with a PhD
- 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.
Desired 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.
**No C2C resumes are considered**
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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Tags: A/B testing APIs Architecture AWS Data pipelines GCP Generative AI Java Machine Learning ML models Model deployment PhD Pipelines Privacy PyTorch SageMaker Security TensorFlow Testing Vertex AI
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