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 job

Google's software engineers develop the next-generation technologies that change how billions of users connect, explore, and interact with information and one another. Our products need to handle information at massive scale, and extend well beyond web search. We're looking for engineers who bring fresh ideas from all areas, including information retrieval, distributed computing, large-scale system design, networking and data storage, security, artificial intelligence, natural language processing, UI design and mobile; the list goes on and is growing every day. As a software engineer, you will work on a specific project critical to Google’s needs with opportunities to switch teams and projects as you and our fast-paced business grow and evolve. We need our engineers to be versatile, display leadership qualities and be enthusiastic to take on new problems across the full-stack as we continue to push technology forward.

Would you like to work on interesting Google products that have a meaningful impact on our users? Then come talk to us! We are building Google's Pixel Watch and Fitbit devices that incorporate the best of Google AI, ML and Fitbit algorithms to enable fitness, wellness, health and overall quality of life for our users and are looking for motivated engineers to join us in our mission.

We 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.

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.