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 |
|---|---|---|
| Research Scientist, Applied ML, Quantum Error Correction Research Scientist role focused on applying machine learning to discover novel Quantum Error Correction (QEC) codes for fault-tolerant quantum computing, specifically for superconducting qubits and high-connectivity platforms. The role involves developing large-scale automated discovery pipelines, optimizing codes for quantum processors, and contributing to the research community through publications and collaborations. | Data | 10 |
| Research Scientist, Visual Data and Generative Research Research Scientist focused on visual data and generative models, involving data acquisition, fine-tuning foundation models for synthetic data generation, and developing automated pipelines for labeling and evaluation datasets. The role emphasizes research in computer vision and machine learning, with a goal of improving generative media and training next-generation architectures. | Data |
| 9 |
| Research Scientist, Visual Data and Generative Research Research Scientist focused on visual data and generative models, specifically for creating high-quality synthetic training data for foundation models. This involves designing data acquisition strategies, optimizing hardware, implementing fine-tuning methods, developing automated labeling pipelines, and creating evaluation datasets for visual quality issues. | DataPost-train | 9 |
| Senior Staff Software Engineer, Machine Learning, ML Training Senior Staff Software Engineer focused on building and delivering ML frameworks for training large language models (LLMs) and stable diffusion models for Google Cloud customers. The role involves designing and implementing AI frameworks software for various ML workloads, identifying and resolving software and performance issues, and collaborating with cross-functional teams. Requires extensive experience in software development, ML design, ML infrastructure, and leading technical projects, with a focus on training ML models at scale. | DataServe | 9 |
| Research Scientist, Protein Design, DeepMind Research Scientist at Google DeepMind focused on using generative machine learning models to design proteins with novel functions for wet-lab testing. The role involves developing models for in silico predictions of protein functions, troubleshooting design failures, and identifying research directions in protein design method development. Requires a PhD in a relevant field and experience with wet-lab experimental procedures and protein design tools. | Data | 8 |
| Software Engineer III, GenAI Data Operations Research, XR Software Engineer III role focused on GenAI Data Operations Research for XR and Platforms & Devices. Responsibilities include leading synthetic dataset deliveries, building toolkits for GenAI inference and metadata extraction, and developing pipelines for fine-tuning foundational models. Requires experience in software development, ML infrastructure, and data processing, with a focus on speech/audio, reinforcement learning, or other ML specializations. | DataPost-train | 8 |
| AI/ML Senior Software Engineer, Data Optimization and Platform This role focuses on improving the time to model quality for users by applying data optimization techniques through integrated tools and platforms. The engineer will scale data optimization techniques, establish technical relationships with product areas, leverage Google Research assets, and collaborate with Research teams and ML practitioners to build and iterate on engineering tools, processing pipelines, and data optimization techniques. The goal is to make AI helpful for everyone by advancing Google's AI capabilities. | DataPost-train | 8 |
| Senior Staff Research Data Scientist, AI Data This role focuses on improving AI model performance through the development of new methodologies for training data, including data acquisition and insights. The Senior Staff Research Data Scientist will work with large datasets, solve complex data science problems, and collaborate with product and model teams to advance AI. | Data | 8 |
| Software Engineering Manager, AI/ML Manage a team of engineers focused on scaling data optimization techniques to improve the performance and quality of ML models. This role involves partnering with research teams and ML practitioners to build engineering tools, processing pipelines, and integration with existing workflows, ultimately supporting user adoption and advancing Google's AI goals. Experience with GenAI infrastructure and ML research/development workflows is preferred. | DataPost-train | 8 |
| Senior Staff Data Scientist Manager, AI Data This role focuses on improving the quality of data used for training Machine Learning models, particularly Large Language Models (LLMs). The responsibilities include analyzing large datasets, defining data quality metrics, researching methods to enhance data quality, and influencing product direction through data insights. The role requires significant experience in data analysis and a background in quantitative fields. | Data | 8 |
| Staff Software Developer, ML Infrastructure, Core Infra Staff Software Developer focused on ML Infrastructure for Core Infra, specifically for conversational AI agents. The role involves driving technical direction for data systems, implementing scalable data solutions on GCP, and leading critical infrastructure investments to support Generative AI development. | Data | 8 |
| Software Engineer, AI/ML, PhD, Early Career Software Engineer role focused on scaling data optimization techniques to improve ML model performance and quality, working with Research teams and ML practitioners to build and iterate on engineering tools and processing pipelines. The role emphasizes the data-centric nature of AI in the Gemini era and improving model quality through data optimization. | Data | 8 |
| Senior Software Engineer, AI/ML Training Infrastructure The role focuses on building the data and training infrastructure for AI/ML models within Google Search. Responsibilities include scaling distributed systems, optimizing training, and applying research to improve model quality and efficiency. The role requires software development experience, particularly with ML infrastructure. | Data | 7 |
| Robotics Hardware Industrialization Engineer, DeepMind The Embodied Systems team within Deepmind Robotics is seeking a Robotics Hardware Industrialization Engineer to bridge the gap between robotics research goals and scalable data collection in a laboratory environment. This role involves transforming advanced research testing concepts into actionable designs and processes for data collection humanoid laboratories, optimizing robot operations, reducing costs, and ensuring system reliability. The engineer will be the first point of contact for test cell design and an authority on process control and DFM/DFX approaches, working with cross-functional teams to define functional hardware and processes for a productive and scalable laboratory environment. | Data | 7 |
| Technical Program Manager, Geo Data Technical Program Manager for the Geo team, focused on architecting and building Generative AI solutions for moderation, scalability, and efficiency. The role involves managing the generation of labels for model improvements, serving as a point of contact for global regulatory workflows, and overseeing end-to-end project lifecycles. Experience with LLMs, prompt engineering, and GenAI moderation frameworks is preferred. | DataPost-train | 7 |
| Senior Leadership Technical Program Manager, AI Data Lead complex projects that explore the profound impact of data on the quality of AI models and products, spanning training, testing, evaluation, and adaptation. Work with engineering, product management, and other partners to define workstreams, requirements, schedules, resources, and milestones. Identify and implement risk mitigations and resolve issues. Provide project status updates to various audiences. | DataPost-train | 7 |
| Senior Data Engineer, GTM Senior Data Engineer role focused on building data pipelines and infrastructure to process unstructured customer feedback data, integrating AI agents and LLMs for insights and routing. The role involves NLP, embedding workflows, and MLOps/LLMOps principles. | DataAgent | 7 |
| Image Processing Engineer Image Processing Engineer role focused on optimizing image quality across hardware and software stacks, fine-tuning 3A algorithms, and building ML automation tools for image quality tuning, testing, benchmarking, and calibration workflows. Requires experience in image quality or computer vision and proficiency in Python and C++. | Data | 7 |
| Software Engineering Manager II, Health Data Foundation Software Engineering Manager II for Google's Health Data Foundation team, focusing on organizing and transforming health and fitness data using AI for Fitbit. The role involves leading a team, architecting technical solutions, and driving cross-functional collaboration to build impactful AI-driven projects. | Data | 7 |
| Security Engineer II, Uppercase Research Security Engineer II, Uppercase Research at Google, focusing on identifying and mitigating threat actors using big data, cybersecurity, machine learning, and large-scale cloud computing. The role involves technical threat actor behavior analysis, data analysis for ML pipelines, building detection engineering processes and tooling, managing YARA-L rules, and innovating detection engineering with LLM-based technologies. | Data | 7 |
| Software Engineer III, Google Threat Intelligence Software Engineer III for Google Threat Intelligence focused on building AI-driven systems for cyber threat collection and analysis. The role involves developing automated tools to gather data from the dark, deep, and open web, applying ML/LLMs to process unstructured text, and building data pipelines to create structured intelligence for threat databases. The goal is to enhance proactive defense against global cyber threats. | Data | 7 |
| Software Engineer, AI/ML Data and Training Infrastructure Software Engineer role focused on building and advancing ML data and training infrastructure to enable ML use cases for recommendation systems. Requires experience in software development, ML models, and ML infrastructure, including data processing, model optimization, evaluation, and deployment. | Data | 7 |
| Staff Software Engineer, ML Data Infrastructure Google's YouTube Discovery Data team is seeking a Staff Software Engineer to build and maintain large-scale data processing pipelines that power personalized discovery and ML models at YouTube. The role involves enabling next-generation model architectures and training procedures, reducing complexity in ML training infrastructure, and collaborating with other infrastructure teams. The ideal candidate will have extensive experience in C++ programming, large-scale infrastructure development, and a solid understanding of ML concepts. | Data | 7 |
| Engineering Manager, Woodshed Engineering Manager for Woodshed, a core AI/ML infrastructure team at Google, focusing on distributed systems for machine learning, dataset lakehouse management (Canon), and supporting multimodal model training for various Google product areas like Gemini and GenMedia. The role involves managing a team, contributing to product strategy, and building next-generation AI/ML systems. | DataPretrain | 7 |
| Image Processing Engineer Image Processing Engineer role focused on optimizing image quality by fine-tuning algorithms and building ML-powered tools for tuning, testing, and calibration workflows. Requires experience in image quality, computer vision, Python, and C++. | Data | 7 |
| Threat Intelligence Engineer, Cloud This role focuses on engineering and scaling systems for threat intelligence within Google Cloud. It involves designing APIs for data ingestion, developing web scrapers for dark/deep web monitoring, and architecting data pipelines that use ML/LLMs to process unstructured web content into structured intelligence. The role also includes deploying and maintaining highly available services and collaborating with threat analysts and data scientists. | DataAgent | 7 |
| Senior Software Engineer, Operations Research Senior Software Engineer role focused on developing and implementing operations research and optimization algorithms, with a component of applying machine learning methodologies to solve problems at Google scale. The role involves writing and testing code, leading design reviews, and contributing to system design and architecture. | Data | 7 |
| Software Engineer III, AI/ML, Health and Home Software Engineer III, AI/ML role focused on developing LLM-based tools for generating synthetic user data and conversations for health and home feature evaluations. This involves implementing ML solutions, utilizing ML infrastructure, and contributing to model optimization and data processing, with a focus on advancing the quality and realism of synthetic context and defining evaluation metrics. | DataAgent | 7 |
