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 |
|---|---|---|
| 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 |
| Software Engineering Manager, Cloud ML Compute Services (Mandarin, English) Software Engineering Manager for Google Cloud's ML Compute Services, focusing on optimizing customer AI/ML models on Google Cloud infrastructure. The role involves leading a team, providing technical guidance, partnering with customers on performance, and collaborating with internal teams to enhance AI workload support. | ServePost-train | 8 |
| Customer Engineer, AI Infrastructure, Google Cloud Customer Engineer focused on deploying and optimizing AI infrastructure (TPUs/GPUs) for customers on Google Cloud Platform, supporting AI training and inference solutions. This role involves deep technical expertise in AI hardware, distributed systems, and performance tuning for large-scale AI workloads. | ServePost-train | 7 |
| Senior Software Engineer, Machine Learning, Debug Senior Software Engineer focused on applying computer vision and deep learning models to analyze mosquitoes for a disease eradication program. The role involves designing, training, and deploying models for object detection and image segmentation, modeling population dynamics, and transitioning research prototypes to production environments. Requires full-stack development experience and experience with ML/CV products, with a preference for edge deployment and biological/environmental science contexts. | ServePost-train | 7 |
| Senior Software Engineer, Machine Learning, Payments Senior Software Engineer, Machine Learning, Payments at Google Singapore. This role focuses on building and maintaining ultra-reliable production ML services and massive batch pipelines for the Payments team. It involves developing ML models, working with product teams for experimentation, analyzing data, and designing experiments. Requires 5 years of software development experience and 3 years each in ML infrastructure and data pipelines for ML. Preferred qualifications include experience with production ML models and system efficiency analysis. | ServeData | 7 |
| Software Engineering Manager, Content Safety, Infra Software Engineering Manager for Content Safety, focusing on ML infrastructure and AI-based techniques to protect users from harmful content. The role involves managing engineers, setting team priorities, developing technical roadmaps, and overseeing the deployment of large-scale projects. Requires significant experience in ML infrastructure, model deployment, evaluation, data processing, and fine-tuning, with a strong emphasis on people management and technical leadership. | ServePost-train | 7 |
| Senior Customer Engineer, AI Infrastructure, Google Cloud Senior Customer Engineer focused on AI infrastructure, specifically Google Cloud TPUs, for enterprise clients. This role involves designing, deploying, and optimizing AI training and inferencing solutions, advising on ML operations, and supporting sales teams by solving technical challenges related to AI hardware and software stacks. | ServePost-train | 7 |
| Staff Software Engineer, Cloud ML Compute Services (Mandarin, English) Staff Software Engineer focused on optimizing AI/ML model performance on Google Cloud infrastructure, including training and inference workloads. The role involves partnering with customers, collaborating with internal teams, and developing training materials, with a strong emphasis on debugging and troubleshooting ML infrastructure. | ServePost-train | 7 |
| Senior Software Engineer, Machine Learning, Debug Senior Software Engineer, Machine Learning, Debug role focused on developing and deploying deep learning models for mosquito-born disease eradication. This involves computer vision for analyzing mosquitoes, statistical modeling for population dynamics, and building production-ready code for scalable deployment, with a focus on optimizing vector control strategies. | ServeData | 7 |
| Senior Customer Engineer, AI Infrastructure, Google Cloud Senior Customer Engineer focused on AI infrastructure, specifically Google Cloud TPUs, for enterprise clients. This role involves designing, deploying, and optimizing AI training and inferencing solutions, advising on ML operations, and supporting sales teams by solving technical challenges related to AI hardware and software stacks. | ServePost-train | 7 |
| Software Engineering Manager, Content Safety, Infra Software Engineering Manager for Content Safety, focusing on AI/ML infrastructure and deployment for protecting users from harmful content. The role involves leading teams, setting technical direction, and ensuring the scalability and reliability of ML systems. | ServePost-train | 7 |
| Software Engineer, Content Safety, Infra Software Engineer role focused on building and scaling content safety platforms using ML infrastructure and responsible AI techniques to protect users from harmful content. The role involves designing, building, and maintaining these platforms, ensuring quality implementations of company-wide standards for Responsible AI, and collaborating with stakeholders. It requires experience in software development, ML infrastructure, and potentially speech/audio or reinforcement learning. | ServeEval Gate | 7 |