About this role
Salary: £160,000 - 200,000 per year
Requirements: Bachelors degree or equivalent practical experience.5 years of experience in machine learning research or research engineering, including experience leading technical projects.3 years of experience training, adapting, or evaluating large-scale foundation models, and building data pipelines for heterogeneous datasets.3 years of experience with modern machine learning frameworks (e.g., JAX, PyTorch, TensorFlow) and distributed training on accelerators.3 years of experience designing evaluations, metrics, and controlled ablations for research projects.Preferred: Masters degree or PhD in Computer Science or related technical field.Preferred: Experience modeling longitudinal or multimodal real-world data (e.g., audio, wearable sensor data, health records).Preferred: Experience profiling and debugging distributed training on TPU or GPU clusters, including handling data noise and training dynamics.Preferred: Domain knowledge for health or fitness, combined with experience in scientific study design and causal inference.Preferred: Record of influential research, deployed ML systems, open-source contributions, or technical leadership in an advanced ML organization. Responsibilities: Design, train, and evaluate machine learning models, owning the full experimentation loop.Develop automated research agents capable of running quantitative evaluations, generating hypotheses, and executing computational experiments.Develop evaluation frameworks that test scientific reasoning, temporal understanding, calibration, generalization, data leakage, and real-world utility.Work with scientists to translate research questions into measurable endpoints and experimental designs.Provide technical leadership through architecture reviews and mentoring.Take ownership in team settings, actively steer technical agendas, and make concrete decisions to overcome technical stalemates.Drive comprehensive model optimization, novel model architectures, and training methods.Transform large-scale time-series data and experimentation systems.Study training dynamics, design evaluations, and turn successful research into systems that operate reliably in production.Develop autoresearch agents that accelerate research workflows and scientific discovery.Work with expert teams to define new endpoints, commission studies, or collect new data when existing datasets cannot answer a question. Technologies: AIMachine LearningPyTorchTensorFlow More:
We are the Health Intelligence team, focused on developing frontier technologies to help everyone live healthier, happier, and longer lives. We build large sensor foundation models to drive scientific discovery for novel health biomarkers and work with world-scale multimodal datasets, including longitudinal sensor data, health agent interactions, and clinical health records. We operate at the forefront of technology with room to innovate, and we are personally invested in the fitness and health space and motivated to improve users lives. We are part of the Health Platforms and Devices team, which builds innovative products and services that help people live longer, healthier lives by combining Google technologies, AI, health behavior science, and user-centered design across apps, services, and health wearables to make consumer health more personal, proactive, and actionable.
last updated 36 week of 2026