Android Developer – Privacy-Preserving & Federated Learning

Android Developer – Privacy-Preserving & Federated Learning

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About Friska AI

FriskaAi is an intelligent health management platform revolutionizing preventive care, chronic disease management, and population health through AI-driven insights. We are part of HFWL Company, committed to transforming healthcare through innovation, compassion, and cutting-edge technology.

We are building next-generation privacy-first mobile health solutions, leveraging Federated Learning (FL) and on-device AI to enable secure, personalized insights without compromising user privacy.

Role Overview

Android Developer with experience in Federated Learning, privacy-preserving AI, or on-device ML

Android development, AI/ML integration, and decentralized intelligence

Key Responsibilities

• Design, develop, and maintain a

  high-performance Android application

  with integrated AI-driven features. 
• Implement

  Federated Learning frameworks

  to enable on-device model training and updates while preserving data privacy. 
• Work with AI/ML engineers to optimize

  on-device inference and training pipelines (TensorFlow Lite, PyTorch Mobile, ONNX Runtime)

  . 
• Ensure

  HIPAA-compliant data privacy and security protocols

  across Android ecosystem. 
• Develop

  synchronization mechanisms

  between Android clients and backend FL infrastructure. 
• Contribute to

  CI/CD pipelines, Play Store deployment, and versioned releases

  . 
• Collaborate with product, design, and clinical teams to ensure seamless integration of health and AI features. 

Requirements

• Bachelor’s or Master’s degree in

  Computer Science, Software Engineering, or related field

  . 

• 2+ years of Android development experience

  using Kotlin and/or Java. 
• Strong understanding of

  Federated Learning concepts, edge AI, or privacy-preserving ML (e.g., secure aggregation, differential privacy)

  . 
• Hands-on experience with

  TensorFlow Lite, PyTorch Mobile, or ML Kit

  . 
• Familiarity with

  Android privacy and security best practices (e.g., Keystore, Scoped Storage)

  . 
• Solid experience with

  RESTful APIs, GraphQL, or

  gRPC

  integrations

  . 
• Proficiency in

  Git, CI/CD tools, and Play Store release management

  . 

Preferred skills

• Experience with

  FL frameworks (TensorFlow Federated, OpenFL, Flower, or

  PySyft

  )

  . 
• Knowledge of

  wearables integration (Wear OS, Google Fit, Health Connect)

  . 
• Experience in

  healthcare, wellness, or fitness mobile apps

  . 
• Familiarity with

  differential privacy, secure computation, or homomorphic encryption

  . 

What you will gain

• Work on a

  cutting-edge mobile health platform using Federated Learning at scale

  . 
• Collaborate with

  AI/ML researchers, healthcare experts, and product teams

  . 
• Shape the future of

  privacy-first AI-driven healthcare on Android

  .