Software Engineer, On-Device Machine Learning

• Collaborate with peers and stakeholders through design and code reviews to ensure best practices amongst available technologies (e.g., style guidelines, checking code in, accuracy, testability, and efficiency).
  
• Implement solutions in one or more specialized ML areas, utilize ML infrastructure, and contribute to model optimization and data processing.
  
• Develop LiteRT, Google's on-device AI framework for first- and third-party, enabling SOTA hardware acceleration and use cases on edge platforms.
• Enable on-device deployment of key models, such as Gemini Nano and Gemma, across various accelerators (GPU/Pixel TPU/NPUs/CPU) on Android, Chrome, iOS, desktop, and more.
• Improve performance of on-device model inference via optimizations in on-device runtime and kernel implementation.

Minimum qualifications:

• Bachelor’s degree or equivalent practical experience.
  
• 2 years of experience with software development in one or more programming languages, or 1 year of experience with an advanced degree.
  
• 2 years of experience with ML infrastructure (e.g., model deployment, model evaluation, optimization, data processing, debugging).
• Experience with runtimes and performance tuning.
• Experience in mobile development.

Preferred qualifications:

• Master's degree or PhD in Computer Science or related technical fields.
  
• Experience in leading and delivering successful ML projects focused on on-device deployment (Android, iOS, web browsers, or embedded devices).
• Experience in ML frameworks (e.g., PyTorch, JAX, TensorFlow).
• Experience with on-device ML SDKs/tooling (e.g., TensorFlow Lite, ExecuTorch, Core ML, SNPE/QNN).
• Strong understanding of Generative AI model architectures and their optimization for on-device execution.
• Passion for innovation and a strong desire to push the boundaries of what's possible with on-device ML.

• Bachelor’s degree or equivalent practical experience.
  
• 2 years of experience with software development in one or more programming languages, or 1 year of experience with an advanced degree.
  
• 2 years of experience with ML infrastructure (e.g., model deployment, model evaluation, optimization, data processing, debugging).
• Experience with runtimes and performance tuning.
• Experience in mobile development.