Minimum qualifications:- Bachelor's degree or equivalent practical experience.
- 5 years of experience in software development, with 2 years of experience delivering AI/ML algorithms for production systems.
- 3 years of experience in C or C for production delivery on embedded targets, and experience using Python for exploratory data analysis, algorithmic modeling, and prototyping.
- 3 years of experience with ML infrastructure (e.g., model deployment, model evaluation, optimization, data processing, debugging).
Preferred qualifications:- Master's degree or PhD in Computer Science or related technical field.
- 5 years of experience with data structures and algorithms.
- Extensive experience optimizing and deploying neural networks directly onto edge devices using specialized frameworks like TensorFlow Lite Micro, or hardware-specific neural processing units.
- Demonstrated track record of driving complex hardware features from early prototype through high-volume manufacturing, including writing low-level sensor drivers and debugging across the hardware-software boundary.
About the jobWe are seeking an expert to enable sensor awareness algorithms using AI/ML bringing real-time sensor fusion understanding to a resource constrained, on-body device. This role is a unique opportunity to take cutting-edge research in sensor AI / ML, often confined to labs and large-scale systems, and ship it in a product with massive, immediate user impact.
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.
Individual pay is determined by factors including job-related skills, experience, and relevant education or training.
US: $174000 - $253000 (USD) 15% bonus target equity benefits
Learn more about benefits at Google .
Responsibilities - Design, train, and evaluate novel AI-based architectures for on-device sensor fusion, gesture recognition, and continuous physiological biosignal monitoring (e.g., IMU, optical sensors).
- Own the full model optimization lifecycle apply advanced knowledge distillation, quantization, and pruning techniques to adapt deep learning models into ultra-low-power formats (TFLite Micro) for efficient, micro-watt edge inference.
- Develop and maintain high-performance, real-time constrained architecture sensor pipelines in C/C , optimizing heavily for latency, power consumption, and memory footprint on wearable MCUs.
- Collaborate with hardware, firmware, product, and UX teams to influence the design of next-generation health sensors and hardware abstraction layers, ensure guaranteed-by-design experience coupled with the physical components.
- Bridge the gap between research scientists and embedded systems engineering, serving as technical lead and subject matter expert for on-device sensor algorithms to guide architectural decisions and define the long-term technical roadmap.
