In this role, you will conduct rigorous power profiling and implement hardware-specific optimizations to ensure feature performance remains consistent across all use cases. You will work at the intersection of ML and system architecture, technical specialists will define the efficiency standards on next-generation hardware. You will optimize the end-to-end performance of ML models to ensure compliance with strict power and thermal constraints, as the scope of the role encompasses leveraging specialized expertise in Hexagon Tensor Processors (HTP) and Digital Signal Processors (DSP) architectures to accelerate machine learning models while maintaining system stability and device performance standards.The Platforms and Devices team encompasses Google's various computing software platforms across environments (desktop, mobile, applications), as well as our first-party devices and services that combine the best of Google AI, software, and hardware. Teams across this area research, design, and develop new technologies to make our user's interaction with computing faster and more seamless, building innovative experiences for our users around the world.
Responsibilities
- Utilise Digital Signal Processors (DSP) and Hexagon Tensor Processors (HTP) architectures to accelerate ML models while effectively minimizing thermal impact.
- Conduct rigorous power profiling and implement hardware-specific optimizations to maintain consistent feature performance across all use cases.
- Own the end-to-end performance optimization of models to operate within a strict power budget.
- Define the efficiency standards on next-generation hardware at the intersection of ML and system architecture.
Minimum qualifications:
- Bachelor’s degree or equivalent practical experience.
- 2 years of experience with software development in C++ or Python programming languages, or 1 year of experience with an advanced degree.
- 1 year of experience with ML infrastructure (e.g., model deployment, model evaluation, data processing, debugging).
Preferred qualifications:
- Master's degree or PhD in Computer Science or related technical fields.
- Experience developing accessible technologies.
- Deep understanding of machine learning principles to ensure algorithms perform within strict power limits.
- Maintaining a current and thorough understanding of the latest developments in Digital Signal Processors (DSP), Hexagon Tensor Processors (HTP), and system-on-chip (SoC) architectures to effectively leverage hardware-specific features for maximum efficiency and thermal management.