Google DeepMind Hiring Research Scientist for Frontier Health Team in California – Salary Range USD 174,000 – USD 252,000 annually

Overview

Google DeepMind is hiring a Research Scientist for its Frontier Health team, based in Mountain View, California. Frontier Health aims to build foundational biomedical intelligence that models human health and disease progression at scale, developing physiological world models and agentic decision systems that can simulate disease evolution and treatment responses. Focus areas include critical care, oncology, metabolic health, disease progression modelling, clinical decision support and multimodal biomedical data, drawing on electronic health records, physiological telemetry and reinforcement learning to move healthcare toward proactive, personalised care.

The scientist carries out fundamental and applied machine learning research on continuous dynamical systems, disease progression, physiological modelling and counterfactual reasoning, working with machine learning experts, software engineers and clinical collaborators across Mountain View, London and Paris, and publishes in leading ML conferences and medical journals. Tasks include designing architectures such as state-space models and neural dynamical systems, analysing clinical telemetry and longitudinal health records, and building benchmarks for disease trajectory prediction and treatment simulation.

What you get

The US salary range is USD 174,000 to 252,000 per year, plus a 15 percent target bonus, equity awards, benefits, professional development and research infrastructure. Pay depends on experience, skills and education.

Requirements

Applicants need a PhD in computer science, machine learning, computational biology, applied mathematics, physics or a related field, or equivalent practical experience, plus at least two years of industry, internship or research experience. They should have built world models, foundation models, state-space models, dynamical systems or deep generative models, know model robustness, uncertainty quantification and out-of-distribution generalisation, and have a strong publication record including first-author papers at venues like NeurIPS, ICML or ICLR. Preferred skills include learning dynamics from partially observed systems, irregularly sampled data, latent state estimation, biomedical datasets such as MIMIC-IV, Python, and JAX, PyTorch or TensorFlow.

How to apply

Candidates apply through Google's careers platform. No deadline is stated.