Implement core infrastructure and conduct research to build generative models of the physical world, focusing on training world simulators and optimizing real-time interactive generation. Collaborate with teams to develop methods for multimodal and video scaling, and create metrics for evaluating physical intelligence.

The Employee Relations Partner at Google DeepMind manages key people processes involving employee investigations, grievances, and performance management while ensuring compliance with US employment law. This role includes advising on employee relations issues, developing training, and improving workplace experiences. The partner works closely with the P&C team and promotes diversity and ethics in the workplace.

Research Scientists at Google DeepMind work on developing algorithms and software prototypes for robotic agents, report research findings, collaborate with teams, and maintain relationships with research labs. The goal is to advance embodied AI technology for practical applications.

As a Research Engineer at Google DeepMind, you will advance AI for Chip Design, developing groundbreaking solutions and collaborating with product teams. You'll solve complex tasks, define project scopes, and share knowledge through publications while contributing to the evolution of Google's chip technology.

As a Senior Hardware Engineer at Google DeepMind, you will collaborate within a team to explore cutting-edge architecture and drive physical design for machine learning acceleration, focusing on design feasibility studies and implementing physical design methodologies.

The Research Scientist will implement infrastructure and conduct research to build generative models of the physical world. Responsibilities include training world simulators, developing metrics for scaling, curating training data, and studying the integration of world models with multimodal language models.

Lead a team responsible for applying advanced models and research in AI to enhance agent memory for Google products, while collaborating closely with engineers and researchers. The role involves rapid-prototyping concepts and running experiments to evaluate opportunities, focusing on several high-impact AI projects.

The Research Scientist will work on advancing Google DeepMind's mission by developing new methods to teach language models to graphically explain concepts, creating evaluation methods, and exploring multimodal AI research in various domains. Responsibilities also include training new models, publishing research, and leading project segments.

As an AI for Code Research Scientist, you will improve models and run experiments to tackle real-world problems, working closely with a team of researchers. Responsibilities include rapid prototyping, foundational research, and contributing to high-impact AI projects such as code generation and natural language processing.

The Research Scientist at Google DeepMind will conduct research to improve the quality and language coverage of multilingual language models. Responsibilities include developing new scalable methods for data validation and evaluation, evaluating model performance, and collaborating with other teams to integrate research findings into various applications.

As a Senior Program Manager, you'll drive strategic initiatives within Google DeepMind Impact Accelerator, partnering with leadership to translate strategy into actionable programs, managing initiatives, leading a team of Program Managers, and fostering collaboration and engagement across the organization.

As a Software Engineer in the Generative AI team at Google DeepMind, you will design and develop scalable software systems for GenAI models. Your responsibilities include optimizing machine learning systems, contributing to novel algorithms, and leading project teams while collaborating across departments. Effective communication and coding skills are essential.

As a Research Engineer at Google DeepMind, you'll advance research in embedding models and multimodal understanding, training AI models for various Google products. Responsibilities include core research in AI, building scalable solutions, and collaborating with product teams to integrate innovative outcomes.

As a Software Engineer at Google DeepMind, you will drive and optimize efficient training of frontier LLMs at scale, improving performance on hardware accelerators and collaborating closely with research and platform teams.

As a Research Engineer at Google DeepMind, you will apply advanced machine learning models to tackle real-world problems, design experiments, and work with product teams. You will collaborate with engineers and researchers, focusing on areas like Generative Media and Reinforcement Learning, while also gaining insights into transitioning research to production.

As a Research Engineer in Media Understanding, you'll conduct core research in computer vision and language understanding. Key responsibilities include training AI models, developing deep learning approaches, and collaborating with teams to create advanced embedding models for Google products, translating research into practical applications.

The role involves applying machine learning techniques to biological sequences, conducting exploratory analysis, reporting research findings, and collaborating with domain experts to enhance protein function prediction models. It suits those with a passion for science and advanced ML methodologies.

In this role, you will partner with Gemini leaders to design and implement people strategies that drive high performance. You'll assess and enhance team leadership effectiveness, solve complex problems, and ensure alignment between people practices and business objectives, fostering a supportive and innovative workplace.

The Staff Robotics Safety Engineer will perform safety risk assessments, implement advanced safety solutions on robots, guide safety policies, and collaborate with researchers to enhance robotics capabilities while ensuring safety in human-centric environments.

The Research Engineer role at Google DeepMind focuses on optimizing model design for Gemini pretraining, enhancing LLM quality, and using various techniques for dataset curation. Responsibilities include inferencing efficiency and collaboration on model preparation stack from pretraining to serving.