Google

Google

Machine Learning Scientist, Consumer Health Research Team

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🌍Seattle, WA, USA
6h ago
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Job Description

Minimum qualifications:

  • PhD degree in Computer Science, a related field, or equivalent practical experience.
  • One or more scientific publication submission(s) for conferences, journals, or public repositories.
  • Experience in leading a research agenda.
  • Coding experience in Python.

Preferred qualifications:

  • 5 years of coding experience in Python.
  • Experience working with Generative AI, health, consumer electronics, medical devices, or technologies.
  • Experience with foundation model development (e.g., LLMs, VLMs, time-series foundation models) or building applications with foundation models.
  • Experience leading research efforts and influencing other researchers.

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.

The Google Consumer Health Research AI Foundations team aims to deliver machine learning research papers as well as models for consumer health tasks. The focus areas for Consumer Health Research Team (CHRT) (e.g., time-series sensor foundation model pre-trained on data from millions of people, large-language model powered generalist agent framework, and comprehensive ML leaderboard for consumer health tasks. The current and ongoing work described below has demonstrated success in applying these models for consumer health use cases.

Google Health is a company-wide effort to help billions of people be healthier. We work toward this vision by meeting people in their everyday moments and empowering them to stay healthy and partnering with care teams to provide more accurate and accessible care. Our teams are applying our expertise and technology to improve health outcomes globally – with high-quality information and tools to help people manage their health and wellbeing, solutions to transform care delivery, research to catalyze the use of artificial intelligence for the screening and diagnosis of disease, and data and insights to the public health community.

The US base salary range for this full-time position is $141,000-$202,000 + 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.
  • Conduct innovative research and development in the field of health technology, primarily focusing on wearables and mobile platforms.
  • Design and develop advanced algorithms for tracking and predicting health-related metrics and outcomes. 
  • Design new architectures for multimodal AI models.
  • Set large-scale tests and deploy ideas quickly and extensively.
  • Develop AI-native health-related products.
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