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.

Our fast-paced team is dedicated to creating the next generation of AI-powered smart glasses. We specialize in rapid research, prototyping, and productionizing. Our mission is to grant users superhuman abilities by enabling seamless, multimodal interactions with Gemini for smart glasses.

As a Senior Research Scientist, you will conduct applied research and translate it into shipping technology, covering a technical spectrum that ranges from compact on-device architectures to custom encoder training and multimodal LM fine-tuning. You will validate concepts through systematic ML experiments and rapid prototyping, then take full ownership of promising projects to transition them from demo to production.

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.

The US base salary range for this full-time position is $166,000-$244,000 + bonus + equity + benefits. Our salary ranges are determined by role, level, and location. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training. Your recruiter can share more about the specific salary range for your preferred location during the hiring process.

Please note that the compensation details listed in US role postings reflect the base salary only, and do not include bonus, equity, or benefits. Learn more about benefits at Google.

Responsibilities

• Design, train and evaluate foundation models and multimodal Large Models (LMs).

• Design and build large-scale distributed ML infrastructure for dataset preparation, model training, and evaluation.

• Design, train, and evaluate machine learning models for on-device applications across various modalities (e.g., audio, sensor signals, text, and images).

• Deploy models on-device and build prototypes and demos for our AI-powered smart glasses.

• Improve model performance and efficiency to meet production specifications.

Minimum qualifications

• PhD degree in Computer Science, a related field, or equivalent practical experience.

• 2 years of experience with frameworks such as JAX or Py Torch.

• Experience with programming in Python and C++.

• One or more scientific publications in machine learning conferences (e.g., NeurIPS, ICML, CVPR, ICCV, Interspeech).

Preferred qualifications

• Experience with on-device machine learning and model optimization for mobile or embedded systems.

• Experience with multimodal LM training (e.g., Supervised Fine-Tuning (SFT), Reinforcement Learning (RL), Low-Rank Adaptation (LoRA)).