Senior Research Scientist, Sensor Technology

Google•Mountain View, CA

3d•$207,000 - $300,000

About The Position

As a Research Scientist at Google, you will lead projects bridging foundational AI, signal processing, and embedded software for future hardware. You will architect multimodal foundation models and ensure they operate efficiently within the strict power, compute, and latency constraints of edge devices. You will partner with hardware technologists to co-optimize AI software pipelines with novel sensor architectures, driving work from research through mass-scale productization. As a technical authority, you will guide cross-functional product and firmware teams in deploying AI-native health features, exploring edge AI research, and improving user experiences through on-device intelligence. The broader research role includes setting up large-scale tests, rapidly deploying promising ideas, managing deadlines and deliverables, and applying current theories to develop products, processes, and technologies. You will create experiments, prototype implementations, design architectures, and contribute to the research community by sharing and publishing findings.

Requirements

PhD in Computer Science, a related field, or equivalent practical experience.
At least 4 years of experience in machine learning research, embedded signal processing, or applied algorithm development for consumer electronics, devices and services, or medical devices.
At least 3 years of experience in technical leadership, guiding research strategy, ML architecture, or algorithmic development for shipping products.
One or more scientific publication submissions to conferences, journals, or public repositories, such as CVPR, ICCV, NeurIPS, ICML, or ICLR.

Nice To Haves

8 years of experience spanning machine learning research, such as time-series foundation models or Generative AI, and embedded software engineering.
Experience in digital signal processing and extracting meaningful features from noisy, real-world data, such as wearable sensor data and physiological signals, using classical and ML-based approaches.
Experience with modern machine learning frameworks such as JAX, PyTorch, or TensorFlow, and deploying algorithms or models onto embedded systems or edge devices.
Experience building data pipelines and designing controlled ablations.
Expertise in edge AI and hardware/software co-design, particularly optimizing ML models for severely power- and memory-constrained environments, such as through model quantization or TinyML.

Responsibilities

Lead research and development at the intersection of machine learning, signal processing, and embedded systems for future health and input technologies across Google's consumer hardware portfolio.
Architect and train multimodal machine learning and foundation models for time-series sensor data, optimizing them for real-time inference on resource-constrained embedded systems.
Drive hardware-software co-design with Specialized Hardware Engineers, ensuring ML architectures and algorithmic requirements inform next-generation sensor definitions and edge-compute selection, such as MCUs, DSPs, and micro-NPUs.
Define long-term technical strategy from cloud-based ML training infrastructure to on-device evaluation, guiding large-scale data collection pipelines and embedded software architecture.
Lead cross-functional integration with Hardware, Firmware, Product, and Clinical teams to deploy optimized ML models and sensing algorithms into consumer-facing edge devices.
Set up large-scale tests, deploy promising ideas, manage deadlines and deliverables, and develop new or improved products, processes, and technologies.
Create experiments, prototype implementations, design architectures, and share and publish research findings.