Job description

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. Frontier Health is developing foundational biomedical intelligence to transform outcomes for complex systemic diseases. We build physiological world models and agentic decision systems that simulate disease advancement and treatment responses across critical care, oncology, and metabolic health. By bridging multi-modal telemetry, EHR data, and reinforcement learning, we are shifting healthcare from reactive observation to proactive intervention. As a Research Scientist, you will advance foundational models for human biology. You will manage problems spanning continuous dynamical systems and counterfactual reasoning to decode pathophysiology. Artificial intelligence will be one of humanity’s most transformative inventions. At Google DeepMind, we are a pioneering AI lab with exceptional interdisciplinary teams focused on advancing AI development to solve complex global challenges and accelerate high-quality product innovation for billions of users. We use our technologies for widespread public benefit and scientific discovery, ensuring safety and ethics are always our highest priority. We are pushing the boundaries across multiple domains. Our global teams offer diverse learning opportunities and varied career pathways for those driven to achieve exceptional results through collective effort. 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 fundamental and applied ML research to develop physiological and behavioral world models simulating continuous-time human biology. • Design novel machine learning architectures (e.g., state-space models, neural dynamical systems) for multi-modal clinical telemetry and longitudinal EHRs (e.g., MIMIC-IV). • Build and maintain robust evaluation benchmarks (OxyBench) to assess disease trajectory predictions, acute clinical events (e.g., sepsis), and counterfactual treatment simulations. • Collaborate cross-functionally with ML researchers, software engineers, and external clinical partners across Mountain View, London, and Paris. • Publish original research in top machine learning conferences and leading medical journals. • PhD in Computer Science, Machine Learning, Computational Biology, Applied Mathematics, Physics, or equivalent practical experience. • 2 years of experience (industry or internships) in building world models for adaptive systems, foundation models, continuous dynamical systems, state-space models, and deep generative architectures. • Experience with model robustness, out-of-distribution generalization, and uncertainty quantification. • Research experience with first-author publications at machine learning venues or domain journals (NeurIPS, ICML, ICLR, etc.). Minimum qualifications: • PhD in Computer Science, Machine Learning, Computational Biology, Applied Mathematics, Physics, or equivalent practical experience. • 2 years of experience (industry or internships) in building world models for adaptive systems, foundation models, continuous dynamical systems, state-space models, and deep generative architectures. • Experience with model robustness, out-of-distribution generalization, and uncertainty quantification. • Research experience with first-author publications at machine learning venues or domain journals (NeurIPS, ICML, ICLR, etc.). Preferred qualifications: • Strong experience learning underlying system dynamics from partially observable environments, managing irregular sampling, missing modalities, and latent state estimation. • Experience applying these methodologies to biomedical domains, framing multi-modal healthcare data, longitudinal EHRs (e.g., MIMIC-IV), and physiological telemetry as complex adaptive systems. • Proficiency in Python and modern deep learning frameworks (JAX, PyTorch, or TensorFlow). • Strong collaborative skills for working in interdisciplinary teams alongside clinical partners. • Passion for AI technology and all of its possibilities.

Requirements

Must-haves

As a Research Scientist, you will advance foundational models for human biology. You will manage problems spanning continuous dynamical systems and counterfactual reasoning to decode pathophysiology.
We are pushing the boundaries across multiple domains. Our global teams offer diverse learning opportunities and varied career pathways for those driven to achieve exceptional results through collective effort.
Individual pay is determined by factors including job-related skills, experience, and relevant education or training.
Learn more about benefits at Google .
Conduct fundamental and applied ML research to develop physiological and behavioral world models simulating continuous-time human biology.
Design novel machine learning architectures (e.g., state-space models, neural dynamical systems) for multi-modal clinical telemetry and longitudinal EHRs (e.g., MIMIC-IV).
Build and maintain robust evaluation benchmarks (OxyBench) to assess disease trajectory predictions, acute clinical events (e.g., sepsis), and counterfactual treatment simulations.
Collaborate cross-functionally with ML researchers, software engineers, and external clinical partners across Mountain View, London, and Paris.
Publish original research in top machine learning conferences and leading medical journals.
PhD in Computer Science, Machine Learning, Computational Biology, Applied Mathematics, Physics, or equivalent practical experience.
2 years of experience (industry or internships) in building world models for adaptive systems, foundation models, continuous dynamical systems, state-space models, and deep generative architectures.
Experience with model robustness, out-of-distribution generalization, and uncertainty quantification.
Research experience with first-author publications at machine learning venues or domain journals (NeurIPS, ICML, ICLR, etc.).
PhD in Computer Science, Machine Learning, Computational Biology, Applied Mathematics, Physics, or equivalent practical experience.
2 years of experience (industry or internships) in building world models for adaptive systems, foundation models, continuous dynamical systems, state-space models, and deep generative architectures.
Experience with model robustness, out-of-distribution generalization, and uncertainty quantification.
Research experience with first-author publications at machine learning venues or domain journals (NeurIPS, ICML, ICLR, etc.).

Nice-to-haves

US: $174000 - $252000 (USD) + 15% bonus target + equity + benefits
Strong experience learning underlying system dynamics from partially observable environments, managing irregular sampling, missing modalities, and latent state estimation.
Experience applying these methodologies to biomedical domains, framing multi-modal healthcare data, longitudinal EHRs (e.g., MIMIC-IV), and physiological telemetry as complex adaptive systems.
Proficiency in Python and modern deep learning frameworks (JAX, PyTorch, or TensorFlow).
Strong collaborative skills for working in interdisciplinary teams alongside clinical partners.
Passion for AI technology and all of its possibilities.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aimlmachine learningpython

Platform: google
Effort: medium