Currently tracking 498 active AI roles, down 12% versus the prior 4 weeks. Primary focus: Agent · Engineering. Salary range $98k–$1030k (avg $233k).
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.
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 |
|---|---|---|
| Research Scientist, Applied ML, Quantum Error Correction Research Scientist role focused on applying machine learning to discover novel Quantum Error Correction (QEC) codes for fault-tolerant quantum computing, specifically for superconducting qubits and high-connectivity platforms. The role involves developing large-scale automated discovery pipelines, optimizing codes for quantum processors, and contributing to the research community through publications and collaborations. | Data | 10 |
| Research Scientist, Visual Data and Generative Research Research Scientist focused on visual data and generative models, involving data acquisition, fine-tuning foundation models for synthetic data generation, and developing automated pipelines for labeling and evaluation datasets. The role emphasizes research in computer vision and machine learning, with a goal of improving generative media and training next-generation architectures. | Data |
| 9 |
| Research Scientist, Visual Data and Generative Research Research Scientist focused on visual data and generative models, specifically for creating high-quality synthetic training data for foundation models. This involves designing data acquisition strategies, optimizing hardware, implementing fine-tuning methods, developing automated labeling pipelines, and creating evaluation datasets for visual quality issues. | DataPost-train | 9 |
| Research Scientist, Protein Design, DeepMind Research Scientist at Google DeepMind focused on using generative machine learning models to design proteins with novel functions for wet-lab testing. The role involves developing models for in silico predictions of protein functions, troubleshooting design failures, and identifying research directions in protein design method development. Requires a PhD in a relevant field and experience with wet-lab experimental procedures and protein design tools. | Data | 8 |
| Senior Staff Research Data Scientist, AI Data This role focuses on improving AI model performance through the development of new methodologies for training data, including data acquisition and insights. The Senior Staff Research Data Scientist will work with large datasets, solve complex data science problems, and collaborate with product and model teams to advance AI. | Data | 8 |