Google has 584 active AI-related job listings. The majority of these roles are focused on agents, representing 40% of the total, and serving infrastructure, at 26%. The most frequent technical tags include model_serving, agent_orchestration, and evals. Over the last 30 days, Google has added 413 new AI roles, a 105% increase compared to the preceding 30-day period.
Currently tracking 498 active AI roles, down 12% versus the prior 4 weeks. Primary focus: Agent · Engineering. Salary range $98k–$1030k (avg $233k).
Google currently has 586 active AI-related roles in our index. The most common open titles are: Software Engineer (5), AI Adoption Customer Engineer, Google Cloud (3), Conversational AI Consultant (2), Engineering Manager, Egregious Abuse Protection (2), Forward Deployed Engineer III, Generative AI, Google Cloud (2). Most positions are in Engineering and Product.
Google's active AI hiring is concentrated in: agents (43%), serving infrastructure (25%), application (19%). These categories follow a seven-stage AI lifecycle: data, pre-training, post-training, serving infrastructure, agents, evaluation, and application.
Google is hiring AI talent in: United States (376 roles), India (53 roles), Singapore (40 roles), Switzerland (20 roles).
Job postings at Google most frequently mention: Software Engineering, Algorithms & Data Structures, System Design, Computer Architecture, Machine Learning.
In the past 30 days, Google has posted 571 new AI-related roles. That is a +22% change versus the prior 30 days (469 → 571).
| Title | Stage | AI score |
|---|---|---|
| AI Research Scientist, Applied AI, Google Cloud Research Scientist role focused on designing, developing, and deploying agentic AI solutions for enterprise use cases within Google Cloud's Applied AI division. The role involves taking ownership of AI quality, implementing and advancing AI techniques, and driving progress through experimentation. It requires a PhD, experience leading research, and applied ML experience, with a focus on enterprise AI solutions and collaboration with model builders. | AgentPost-train | 9 |
| Staff AI Research Scientist, Applied AI, Google Cloud Staff AI Research Scientist role focused on designing, developing, and deploying scalable and agentic AI solutions for enterprise use cases within Google Cloud's Applied AI team. The role involves taking ownership of AI quality for production systems, implementing evaluation frameworks, and advancing AI techniques through experimentation. It requires a PhD, experience with ML algorithms and LLMs, and a publication record, with a focus on applied research and product contribution. |
| AgentEval Gate |
| 9 |
| Research Engineer, Frontier Safety Mitigations, DeepMind Research Engineer focused on developing and deploying advanced safety mitigations for frontier AI models, specifically defending against misuse in domains like Cybersecurity and CBRNE. The role involves building classifiers, data pipelines, monitoring systems, and evaluating agentic AI systems, with a strong emphasis on automated red-teaming and adversarial robustness. | AgentEval Gate | 9 |
| Research Scientist, Robotics, DeepMind Research Scientist at Google DeepMind Robotics focused on building foundation models like vision-language-action (VLA) models for physical agents, enabling robots to perceive, plan, think, use tools, and act. The role involves research into agentic reasoning, real-world understanding, and robot control, with a focus on publishing research and contributing to the AI research community. | AgentPost-train | 9 |
| Research Engineer, Embodied Agents, DeepMind Research Engineer at DeepMind focused on building AI agents that operate in physical environments, advancing physical AGI. The role involves designing learning infrastructure, collaborating with partners for deployment, and building developer tools for robotics capabilities. Key challenges include bridging the gap between simulation and real-world robot performance. | AgentServe | 9 |
| Research Scientist, Manipulation for Robotics, DeepMind Research Scientist role focused on pioneering AI integration into robotics for physical agents, enabling robots to perceive, plan, think, use tools, and act. The role involves developing and training large foundation models for general-purpose dexterous manipulation tasks, aiming for human-level performance on real-world applications using frontier robotics models. This includes working with simulators, real robots, and various AI techniques like reinforcement learning and vision-language-action models. | AgentData | 9 |