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 |
|---|---|---|
| Machine Learning Hardware Architect, Hardware, Software Co-Design, Google Cloud This role focuses on architecting and defining the roadmap for AI/ML hardware acceleration, specifically TPUs, for Google Cloud. It involves co-design between model architecture and next-generation hardware, optimizing for ML serving and training capabilities, and integrating large-scale foundation models with advanced silicon architectures. The role requires defining technical roadmaps, architecting simulation frameworks, guiding system-level performance analysis, and managing cross-functional partnerships across hardware, compiler, and ML teams. | ServePost-train | 9 |
| Forward Deployed Engineer II, GenAI, Google Cloud Forward Deployed Engineer II, GenAI, Google Cloud - This role involves building and deploying bespoke agentic AI solutions for enterprise customers, managing integration complexities, data readiness, and state management. The engineer will act as a builder-consultant, transitioning prototypes to production, architecting connective tissue between AI products and customer infrastructure, and building evaluation and observability pipelines. The role also involves identifying field patterns to inform product roadmaps and mentoring talent. |
| AgentServe |
| 9 |
| Staff Data Scientist, GenAI and Agentic SOC Staff Data Scientist role focused on Generative AI and Agentic Security Operations Center (SOC). The role involves driving ML/AI initiatives, building and scaling infrastructure for developing, fine-tuning, and deploying autonomous security agents, and creating testing/evaluation methodologies for LLMs and agentic workflows in security environments. Collaboration with cross-functional teams and mentoring are also key aspects. | AgentEval Gate | 9 |
| Technical lead, Google Cloud Security Technical lead for Google Cloud Security, focusing on transitioning to an AI-native Security Operations Center (SOC). The role involves architecting an Agent Engine and universal APIs to enable enterprise security teams to orchestrate defense at machine speed, shifting from static workflows to multi-agent ecosystems. | Agent | 8 |
| Software Engineer III, AI/ML GenAI, Google Research Software Engineer III at Google Research focused on implementing GenAI solutions, utilizing ML infrastructure, and contributing to data preparation, optimization, and performance enhancements. The role involves core GenAI concepts like LLMs and Multi-Modal models, with experience in text, image, video, or audio generation being key. The position is primarily focused on serving AI models (L3) with a secondary involvement in post-training aspects (L2). | ServePost-train | 8 |
| Machine Learning SoC Architect, Google Cloud Silicon This role focuses on the architecture of Machine Learning System-on-Chips (SoCs), specifically TPUs, for AI/ML hardware acceleration. The individual will shape the future of AI/ML hardware, drive edge-AI product development, and work on distributed inference technology for real-time systems. Responsibilities include developing architectural specifications, owning IP architecture, collaborating with algorithm teams, performing architecture studies, and leading architectural blocks through the product lifecycle. | Serve | 8 |
| Product Manager II, AI Crisis Resilience Product Manager to define and execute strategy for AI-powered crisis response experiences across Google Search, focusing on timely, accurate, and life-saving information during environmental and humanitarian crises. This role involves leading the development of AI-first systems, partnering with engineering and research teams, and balancing innovative AI solutions with data accuracy and trustworthiness. | Ship | 7 |
| Research Data Scientist II, Waze This role focuses on developing and owning Machine-Learning models for Waze's personalized navigation experience. Responsibilities include feature engineering, model evaluation, tuning, monitoring, and working with product and backend teams to integrate models into production. The role requires experience in data engineering, ML, and cloud platforms. | Post-train | 7 |
| Senior Software Engineer, Duplex Senior Software Engineer role at Google focused on developing AI-powered consumer products, specifically leveraging speech/audio technology and ML infrastructure. The role involves writing and testing code, collaborating with stakeholders, debugging issues, and designing/implementing ML solutions. Requires experience in software development, speech/audio, reinforcement learning, or ML infrastructure, with a focus on product development and ML infrastructure. | ShipServe | 7 |
| Software Engineer III, Duplex Software Engineer III at Google working on the Duplex product, which involves building phone conversation bots. The role requires creating datasets, evaluating AI model quality, and building Python backends for communication within the Geo ecosystem. Collaboration with other engineering teams across Geo, Search, and Research is expected to advance Duplex capabilities. | AgentData | 7 |
| Staff Software Engineer, Google Cloud Storage, AI/ML Staff Software Engineer at Google Cloud Storage, focusing on building and optimizing storage solutions for AI/ML workloads, bridging core storage infrastructure with the demands of training and inference. | ServeData | 7 |
| Senior Software Engineer, Google Cloud, Generative AI, Blackbelt Senior Software Engineer, Google Cloud, Generative AI, Blackbelt role focused on helping customers adopt and integrate Google's Generative AI solutions, including AI Agents and Gemini Enterprise. The role involves understanding customer needs, providing technical expertise, developing integration strategies, and supporting developers in leveraging Generative Language APIs. It requires experience in software development, Python, data science/ML, and specifically with GenAI and AI Agents. | Agent | 7 |
| Technical Lead, Google Cloud Security Technical Lead for Google Cloud Security, focusing on transitioning to an AI-native Security Operations Center (SOC). The role involves architecting an Agent Engine and universal APIs for enterprise security teams to orchestrate defense at machine speed, shifting from static workflows to multi-agent ecosystems. Requires strong software development, ML design, and architecture experience, with a focus on AI/ML integration into enterprise products. | AgentServe | 7 |
| SoC Vision Architect, Silicon, Google Cloud This role focuses on architecting the hardware (SoC Vision Architect) for AI/ML applications, specifically Tensor Processing Units (TPUs) and their associated imaging pipelines (ISP, CODECS). The goal is to define custom silicon solutions that power Google's demanding AI/ML workloads, optimizing for power, performance, and area while integrating advanced computational imaging algorithms and potentially deploying neural networks on specialized hardware. | Serve | 7 |