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 |
|---|---|---|
| Pretraining codebase SWE, DeepMind Software Engineer at Google DeepMind focused on the pretraining codebase for a specific model (Cider), involving handover with Apple, IP obfuscation, eval correctness, and integration with Apple's deployment. The role involves writing and testing code, contributing to documentation, and code reviews, with a focus on enabling research and deploying promising ideas. | Pretrain | 9 |
| Senior Software Engineer, Machine Learning, Core ML Senior Software Engineer on the RecML team, focused on scaling machine learning for recommendations and user modeling. The role involves architecting and implementing model-parallel training, optimizing transformer models, and writing low-level code for performance. This is a horizontal ML infra and efficiency role supporting the training framework of foundation recommender models. | Pretrain |
| 8 |