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 |
|---|---|---|
| Hardware Architecture Modeling Engineer, PhD, University Graduate This role focuses on developing architectural and micro-architectural models for next-generation TPUs (Tensor Processing Units) to enable quantitative analysis of performance and power. The engineer will contribute to Machine Learning workload characterization, benchmarking, and hardware-software co-design, collaborating with various teams to define TPU chip specifications and roadmaps for AI/ML hardware acceleration. | Serve | 7 |
| Software Engineer III, AI/ML Recommendations, Rankings, Predictions, YouTube Software Engineer III at Google working on AI/ML Recommendations, Rankings, and Predictions for YouTube. The role involves writing product/system code, collaborating with peers, contributing to documentation, triaging issues, and building/deploying recommendation systems models using ML infrastructure, with a focus on model optimization and data processing. Requires a Bachelor's degree, 2 years of Python/C++ experience, 1 year of experience building/deploying recommendation systems, and 1 year of ML infrastructure experience. |
| ShipData |
| 7 |
| Staff Software Engineer, AI/ML Recommendations, Rankings, Predictions, YouTube Staff Software Engineer at Google working on AI/ML for YouTube Recommendations, Rankings, and Predictions. The role involves designing, developing, and deploying large-scale software solutions, providing technical leadership, and optimizing ML infrastructure. Key responsibilities include leading the design and implementation of recommendation systems, optimizing ML infrastructure, and guiding model architecture development. Requires significant experience in building and deploying recommendation systems models in production and leading ML design and optimization. | ShipAgent | 7 |
| Software Engineering Manager II, AI/ML Recommendations, Rankings, Predictions, YouTube Software Engineering Manager II for AI/ML Recommendations, Rankings, and Predictions at YouTube. This role involves leading a team of engineers, setting team priorities, developing technical roadmaps, designing and implementing recommendation systems, optimizing ML infrastructure, and guiding model architecture development. Requires experience in software development, ML design, ML infrastructure optimization, building and deploying recommendation systems, and technical/people leadership. | ShipServe | 7 |
| Senior Software Engineer, AI/ML Recommendations, Rankings, Predictions, YouTube Senior Software Engineer focused on building and deploying recommendation systems models for YouTube, leveraging ML infrastructure and contributing to architecture design. This role involves coding, testing, debugging, and designing models for retrieval, prediction, ranking, and personalization. | ShipData | 7 |