Minimum qualifications: Bachelor's degree or equivalent practical experience. 8 years of experience in software development. 5 years of experience testing, and launching software products, and 3 years of experience with software design and architecture. 5 years of experience leading ML design and optimizing ML infrastructure (e.g., model deployment, model evaluation, data processing, debugging, fine tuning). 2 years of experience with GenAI techniques (e.g., LLMs, Multi-Modal, Large Vision Models) or with GenAI-related concepts (language modeling, computer vision). Preferred qualifications: Master’s degree or PhD in Engineering, Computer Science, or a related technical field. 8 years of experience with data structures and algorithms. 3 years of experience in a technical leadership role leading project teams and setting technical direction. 3 years of experience working in a complex, matrixed organization involving cross-functional, or cross-business projects. Experience with Python and one or more of the following: Large Language Models (LLMs), Machine Learning, or Generative AI. About the job Google Cloud's software engineers develop the next-generation technologies that change how billions of users connect, explore, and interact with information and one another. We're looking for engineers who bring fresh ideas from all areas, including information retrieval, distributed computing, large-scale system design, networking and data storage, security, artificial intelligence, natural language processing, UI design and mobile; the list goes on and is growing every day. As a software engineer, you will work on a specific project critical to Google Cloud's needs with opportunities to switch teams and projects as you and our fast-paced business grow and evolve. You will anticipate our customer needs and be empowered to act like an owner, take action and innovate. We need our engineers to be versatile, display leadership qualities and be enthusiastic to take on new problems across the full-stack as we continue to push technology forward. The Vertex AI team empowers developers to build next-generation AI solutions. Partnering with Google DeepMind and Cloud AI Research, we bridge scalability and orchestration gaps across GCP (including Gemini and Workspace) to establish Google as the GenAI Agent market leader. As a Staff Software Engineer on the Orcas Agent Platform team, you will provide critical technical leadership. You will design sophisticated infrastructure and frameworks for GenAI agents, addressing scalability, reliability, and efficiency challenges. You will develop novel tools using the Agentic Development Kit (ADK) and Agent Engine to enable advanced, tool-using agents. Cross-functionally, you will influence product direction, advance techniques, and drive high-impact technology adoption for our enterprise and developer ecosystem. The Google Cloud AI Research team addresses AI challenges motivated by Google Cloud’s mission of bringing AI to tech, healthcare, finance, retail and many other industries. We work on a range of unique problems focused on research topics that maximize scientific and real-world impact, aiming to push the state-of-the-art in AI and share findings with the broader research community. We also collaborate with product teams to bring innovations to real-world impact that benefits our customers. Individual pay is determined by factors including job-related skills, experience, and relevant education or training. US: $207000 - $301000 (USD) + 20% bonus target + equity + benefits Learn more about benefits at Google. Responsibilities Improve the reliability, scalability and efficiency of Orcas Agent Runtime Platform. Work cross-functionally with product managers, UX designers, and infrastructure teams to ship reliable, scalable, and highly usable services. Architect and develop novel evaluation and customization solutions, leveraging advanced techniques and frameworks like Agency/Chiliagon and Agentic Development Kit (ADK) to push the boundaries of model quality assessment ( Cloud Lifecycle AI Tools). Mentor and provide technical guidance to other engineers and scientists on the team on design techniques, coding standards, and ML best practices. Collaborate closely with research teams to translate state-of-the-art techniques into tangible, real-world product impact.