The role
From Google's own posting.
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 team generates novel health insights and builds foundation models (wearables and Electronic Health Records (EHR)) using large datasets from nationwide studies. We publish our research breakthroughs and partner cross-functionally to translate them into products that improve millions of lives. You will collaborate alongside researchers, engineers, and the Gemini team.
In this role, you will work at the intersection of foundational AI and health research. You will develop novel multi-modal model architectures, perform experiments, and apply advanced theories to build AI-native products. Partnering with product teams, you will advance foundational AI capabilities, publish research findings, and collaborate to enhance healthcare outcomes.
The Health Platforms and Devices team builds innovative products and services that help our users live longer, healthier lives. We bring together the best of Google technologies and AI, health behavior science, and user-centered design to help users organize the health and wellness data, get insight from it, and take action toward their health goals. We do this with a suite of apps, services, and health wearables. We aim to make consumer health more personal, proactive, and actionable.Individual pay is determined by factors including job-related skills, experience, and relevant education or training.
US: $174000 - $252000 (USD) + 15% bonus target + equity + benefits
Learn more about benefits at Google .
Conduct AI research and development in the field of health technology, primarily focusing on wearables and mobile platforms, for tracking and predicting health-related metrics and outcomes.
Design architectures for multimodal AI models and set up tests to deploy ideas broadly, while building and improving AI research infrastructure by ingesting datasets and maintaining evaluation systems.
Communicate research findings to a wide variety of audiences, and publish findings in academic journals.
Identify upcoming research areas by interacting with potential collaborators to help develop long-term research strategy and plans that expand the impact of Google research.
Collaborate with cross-functional partners on engineering, product, and clinical teams to integrate research developments into consumer-facing products.
Minimum qualifications:
PhD degree in Computer Science, Biomedical Engineering, or a related quantitative field.
2 years of experience with modern machine learning frameworks such as JAX, PyTorch, or TensorFlow and distributed training on accelerators.
One of more scientific publication submission(s) for conferences, journals, or public repositories (such as CVPR, ICCV, NeurIPS, ICML, ICLR, etc.).
Preferred qualifications:
5 years of experience in machine learning research, foundation models (e.g. LLMs, VLMs, time-series foundation models), Generative AI, and coding experience in Python in a team-based environment.
Experience modeling longitudinal or multimodal real-world data such as wearable sensor data, physiological signals, health records, imaging.
Experience building data pipelines and experimentation systems for large, heterogeneous datasets.
Experience designing evaluations, metrics, and controlled ablations that produce reproducible and defensible research results.
PhD degree in Computer Science, Biomedical Engineering, or a related quantitative field.
2 years of experience with modern machine learning frameworks such as JAX, PyTorch, or TensorFlow and distributed training on accelerators.
One of more scientific publication submission(s) for conferences, journals, or public repositories (such as CVPR, ICCV, NeurIPS, ICML, ICLR, etc.).