Senior Research Scientist, Sensor Technology

About the job

As an organization, Google maintains a portfolio of research projects driven by fundamental research, new product innovation, product contribution and infrastructure goals, while providing individuals and teams the freedom to emphasize specific types of work. As a Research Scientist, you'll setup large-scale tests and deploy promising ideas quickly and broadly, managing deadlines and deliverables while applying the latest theories to develop new and improved products, processes, or technologies. From creating experiments and prototyping implementations to designing new architectures, our research scientists work on real-world problems that span the breadth of computer science, such as machine (and deep) learning, data mining, natural language processing, hardware and software performance analysis, improving compilers for mobile platforms, as well as core search and much more.

As a Research Scientist, you'll also actively contribute to the wider research community by sharing and publishing your findings, with ideas inspired by internal projects as well as from collaborations with research programs at partner universities and technical institutes all over the world.

As a Research Scientist at Google, you will lead projects bridging foundational AI, signal processing, and embedded software for future hardware. You will architect multi-modal foundation models, ensuring they operate efficiently within the strict power, compute, and latency constraints of edge devices.

Partnering with hardware technologists, you will co-optimize AI software pipelines with novel sensor architectures driving the lifecycle from research to mass-scale productization. As a technical authority, you will guide cross-functional product and firmware teams to deploy AI-native health features, explore edge AI research, and enhance user experiences through on-device intelligence.

US: $207000 - $300000 (USD) + 20% bonus target + equity + benefits

Learn more about benefits at Google.

Responsibilities

• Lead innovative 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 tailored for time-series sensor data, while actively optimizing them for real-time inference on resource-constrained embedded systems.
• Drive hardware-software co-design by partnering tightly with Specialized Hardware Engineers, ensuring that ML architectures and algorithmic requirements directly influence next-generation sensor definitions and edge-compute selection (e.g., MCUs, DSPs, micro-NPUs).
• Define the long-term technical strategy spanning cloud-based ML training infrastructure down to on-device evaluation, guiding large-scale data collection pipelines and embedded software architecture.
• Lead cross-functional integration efforts with Hardware, Firmware, Product, and Clinical teams to deploy highly optimized ML models and sensing algorithms into consumer-facing edge devices quickly and broadly.

Qualifications

Minimum qualifications:

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

Preferred qualifications:

• 8 years of experience spanning machine learning research (e.g., time-series foundation models, Generative AI) and embedded software engineering.
• Experience in digital signal processing (DSP) and extracting meaningful features from noisy, real-world data (e.g., wearable sensor data, physiological signals) using both classical and ML-based approaches.
• Experience with modern machine learning frameworks such as JAX, PyTorch, or TensorFlow, alongside experience 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 (e.g., model quantization, TinyML).