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 |
|---|---|---|
| Staff Research Scientist, ML Efficiency, Google Research Research Scientist focused on improving the computational efficiency of large-scale generative AI models (LLMs, Diffusion Models, Generative Videos) through advanced algorithms, model compression, quantization, and optimization of training and inference pipelines. Collaborates with hardware and software teams to optimize kernels and inference engines. | ServePost-train | 9 |
| Research Scientist, ML Efficiency, Google Research Research Scientist focused on improving the computational efficiency of large-scale Generative AI Models (LLMs, Diffusion Models, Generative Videos) through algorithmic research, model compression, and inference acceleration. The role involves advancing algorithms for serving and inference, innovating training architectures, optimizing deployment pipelines, and collaborating with hardware/software teams. A PhD and publication record are required. |
| ServePost-train |
| 9 |
| Senior Research Scientist, ML Efficiency, Google Research Research Scientist focused on improving the computational efficiency of generative AI models (LLMs, Diffusion Models, Generative Videos) through foundational research in algorithmic efficiency, model compression, and inference acceleration. This role involves innovating algorithms, optimizing model architectures, improving the deployment pipeline (pretraining, tuning, RL), and collaborating with hardware/software teams to optimize inference engines and reduce latency/memory usage. | ServePost-train | 9 |
| Research Software Engineer, Google Research Research Software Engineer at Google Research focused on pioneering AI research in Singapore, specifically on developing performant, efficient, and capable generative AI models. The role involves abstracting problems, designing solutions, prototyping, profiling, benchmarking, and collaborating with global research and product teams. | Pretrain | 9 |
| Senior Research Scientist, App and Ecosystem Trust Research Scientist role focused on Android security and AI agent safety, developing and publishing novel research, designing neuro-symbolic and agent-powered systems for threat detection, and using machine learning for code analysis to combat malware and evasion techniques within the Android ecosystem. | Agent | 8 |
| Staff Research Scientist, App and Ecosystem Trust Research Scientist role focused on protecting Android users from abusive apps by developing and publishing novel research on Android security and AI agent safety. The role involves designing neuro-symbolic and agent-powered systems, and using machine learning for malware detection and evasion technique neutralization. | Agent | 8 |
| Senior Staff Research Scientist, App and Ecosystem Trust Research Scientist role focused on Android security and AI agent safety, developing novel research and agent-powered systems to detect and prevent various forms of abuse. The role involves analyzing codebases using ML and collaborating with internal and external partners. | Agent | 8 |
| Staff Research Scientist, App and Ecosystem Trust Research Scientist focused on protecting Android users from abusive apps by developing and publishing novel research on Android security and AI agent safety. The role involves designing neuro-symbolic and agent-powered systems, analyzing codebases using machine learning, and collaborating with internal and external partners to improve defenses against evolving threats. | Agent | 8 |
| Senior Data Scientist, Research, App Ecosystem and Trust This role focuses on applying advanced quantitative methods and machine learning to protect Android users from abusive applications. The data scientist will conduct end-to-end analysis, including model development and evaluation, to detect and prevent various forms of abuse, impacting the safety of billions of users. | Post-train | 7 |
| Data Scientist Manager, Research, App Ecosystem and Trust Manager for a Data Science Research team focused on protecting Android users from AI-driven threats like malware and impersonation. The role involves leading technical strategy, identifying impactful data science problems, and driving the development of advanced quantitative methods, including ML modeling, to create data-driven products and solutions. This involves cross-functional collaboration with Engineering and Product leaders to translate research into business decisions. | Ship | 7 |
| Senior Data Scientist, Research, App Ecosystem and Trust This role focuses on applying advanced quantitative methods and machine learning to protect Android users from abusive applications. The data scientist will conduct end-to-end analysis, develop and evaluate ML models, and collaborate with product and engineering teams to improve safety systems. The work involves handling large datasets in an adversarial environment, aiming to detect and prevent various forms of abuse. | Post-train | 7 |