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 |
|---|---|---|
| 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 |
| 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 |
| 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 |
| 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 |