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 |
|---|---|---|
| Software Engineer III, Generative AI, Search Health Software Engineer III for Generative AI in Google Search Health, focusing on improving response quality, implementing new features, prompt engineering, and collaborating with the core model team. Responsibilities include building datasets, analyzing behavior, and running A/B experiments. Requires experience with GenAI techniques and ML infrastructure. | Post-trainAgent | 8 |
| ML Software Engineer, GenAI for Youth ML Software Engineer focused on building scalable, responsible AI frameworks for youth-focused products. The role involves developing unified infrastructure, algorithmic steering, and automated validation layers to bridge foundational research with production environments, operating across global product lines and acting as a technical advisor. |
| Post-trainServe |
| 8 |
| Senior Staff Engineering Analyst Manager, Gemini, NanoBanana, Veo Senior Staff Engineering Analyst Manager for Gemini, NanoBanana, and Veo, focusing on AI model mitigations and post-training. This role involves leading a team, pioneering safety technologies, driving cross-functional alignment, directing launch strategies for foundational models, and applying data science to audit model safety and uncover vulnerabilities. | Post-train | 8 |
| Software Engineer Software Engineer at Google DeepMind focused on applying research to build next-generation GenAI features. Responsibilities include prototyping GenAI solutions, curating datasets, building ML pipelines for generative media and multimodal understanding, developing product code, and performing comprehensive testing. Requires experience in developing ML models with Tensorflow/PyTorch/JAX and applying ML/statistics/diffusion model theory. | Post-trainData | 8 |
| Software Engineer Software Engineer at Google DeepMind focused on applying research to high-impact problems by prototyping GenAI solutions, curating datasets, and building ML pipelines for generative media, multimodal understanding, and reinforcement learning. Requires experience in ML, software development, data structures, algorithms, and ML infrastructure management. | Post-trainData | 8 |
| Software Engineer Software Engineer at Google DeepMind focused on applying research to high-impact problems by prototyping GenAI solutions, curating datasets, and building ML pipelines for generative media, multimodal understanding, and reinforcement learning. Requires experience in training generative AI models, developing ML models with frameworks like Tensorflow/PyTorch/JAX, ML infrastructure management, and software development. | Post-trainData | 8 |
| Software Engineer Software Engineer at Google DeepMind focused on applying research to high-impact GenAI problems, including prototyping solutions, curating datasets, and building ML pipelines for generative media, multimodal understanding, and reinforcement learning. The role involves developing robust product code, performing comprehensive testing, collaborating with peers, resolving system issues, creating documentation, and managing the full deployment lifecycle. | Post-trainData | 8 |
| Software Engineer, PhD, Early Career, AI/Machine Learning, 2026 Start Google is seeking PhD graduates for Software Engineer roles focused on AI/Machine Learning. The role involves developing and deploying advanced ML systems across the full stack, from hardware acceleration to production APIs, and transforming research expertise into scalable products. The position requires experience in ML/AI and coding, with a preference for large-scale distributed environments and deep learning frameworks. | Post-trainServe | 8 |
| Software Engineer III, Perception, XR Software Engineer III, Perception, XR at Google, focusing on on-device deep learning for audio enhancement and speech understanding in smart wearable glasses. The role involves developing and evaluating speech enhancement algorithms, experimenting with ML modeling, and acquiring data for training models. | Post-trainData | 7 |
| Business Data Scientist, Customer Voice, Analytics This role focuses on developing and deploying NLP and machine learning models to extract insights from customer conversations for a Go-to-Market team. It involves identifying trends, predicting customer behaviors, and ensuring statistical rigor in reporting, with a focus on Large Language Models and prompt engineering. | Post-trainData | 7 |
| Senior Software Engineer, Motion Algorithms Senior Software Engineer focused on developing and productizing cutting-edge ML models and algorithms for motion and location systems on Android devices and Pixel products. The role involves working on sensor fusion, state estimation, and optimizing these for resource-constrained environments, impacting core features and user experiences. | Post-trainServe | 7 |
| Engineering Manager, Ads ML Modeling Engineering Manager for Google's Ads ML Modeling team, responsible for building and maintaining models to improve advertiser campaign optimization. The role involves people management, technical leadership, strategic planning, and driving the development of ML applications, data analyses, and methodologies. Focuses on areas like forecasting, predictive caching, and UI ranking models. | Post-train | 7 |