Staff Software Engineer, Agentic AI
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Minimum qualifications:
- Bachelor’s degree or equivalent practical experience.
- 8 years of experience in software development.
- 5 years of experience with GenAI techniques (e.g., LLMs, Multi-Modal, Large Vision Models), with GenAI-related concepts (language modeling, computer vision), speech/audio (e.g., technology duplicating and responding to the human voice), ML infrastructure, or experience in distributed systems.
- 5 years of experience leading to ML design and optimizing ML infrastructure (e.g., model deployment, model evaluation, data processing, debugging, fine tuning).
- 5 years of experience testing, and launching software products.
- 3 years of experience with software design and architecture.
Preferred qualifications:
- Master’s degree or PhD in Engineering, Computer Science, or a related technical field.
- 8 years of experience with data structures/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.
About the job
Google's software engineers develop the next-generation technologies that change how billions of users connect, explore, and interact with information and one another. Our products need to handle information at massive scale, and extend well beyond web search. 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’s needs with opportunities to switch teams and projects as you and our fast-paced business grow and evolve. 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.Our team, Project Agentis, is moving AI from simply thinking and reasoning to actively doing so. Our goal is to build and deploy a suite of collaborative, enterprise-grade AI agents that own and execute core business outcomes across functions like sales, procurement, and finance. We believe that enterprise work is shifting from human-centric processes to agentic, outcome-driven execution, where humans are elevated to strategic conductors.
We want to enable not only Google, but also our enterprise customers towards this goal. To make this goal a reality, we are also building Polysynth, an intuitive no-code platform that will empower non-technical business users to also create, deploy, and manage these next-generation agents. This requires a new, incredibly robust, and scalable back-end infrastructure.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.
Responsibilities
- Build the user-facing components and interfaces for the Agent API, enabling users to design and manage AI agents. Rapidly prototype and iterate on full-stack features that abstract complex GCP environments.
- Develop the core for multi-agent orchestration, real-time data analysis, and autonomous tool use. Implement foundational capabilities that allow agents to reason over and act on data from various enterprise sources.
- Ensure agent actions are transparent and trustworthy for enterprise customers by building robust monitoring, logging, and explainability features. Collaborate with Google DeepMind and Vertex/Orcas to shape the future of AI frameworks.
- Explore and implement new AI/ML technologies, bringing fresh ideas to solve ambiguous problems and ensure high reliability and performance.
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