Job description

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. The Hybrid Cloud Environment team's mission is to empower Googlers and AI agents to build faster, safer, and more efficiently on Google Cloud. We develop and operate Reef, Alphabet's unified, cloud-native development platform. Reef abstracts the complexities of infrastructure, security, and financial management, enabling users—from human developers to autonomous coding agents in Gemini—to go from high-level intent to a running, production-ready, and compliant Google Cloud environment in minutes. You will directly empower frontier AI teams like Google DeepMind and Gemini to build cloud-native applications with startup agility. The Core team builds the technical foundation behind Google’s flagship products. We are owners and advocates for the underlying design elements, developer platforms, product components, and infrastructure at Google. These are the essential building blocks for excellent, safe, and coherent experiences for our users and drive the pace of innovation for every developer. We look across Google’s products to build central solutions, break down technical barriers and strengthen existing systems. As the Core team, we have a mandate and a unique opportunity to impact important technical decisions across the company. Individual pay is determined by factors including job-related skills, experience, and relevant education or training. US: $207000 - $300000 (USD) + 20% bonus target + equity + benefits Learn more about benefits at Google . • Translate high-level developer and agent intent into deterministic, verifiable environments. • Architect fleet-wide drift detection, formal policy verification, and autonomous IAM/security remediation agents across Alphabet's multi-organization footprint. • Design unified policy and budget decorators so greenfield environments are safe-by-default and cost-governed at creation without manual reviews. • Build high-scale TrueCost billing attribution, ephemeral environment TTL enforcement, and automated brownfield waste reclamation. • Build evaluation harnesses to benchmark agentic infrastructure actuation. Mentor engineers and align technical roadmaps. • Bachelor's degree or equivalent practical experience. • 8 years of experience designing, building, and operating large-scale distributed systems, with a focus on scalability, fault tolerance, and reliability. • Experience designing software architecture for applications, including AI/ML systems. • Experience in cloud computing, with a focus on AI/ML, data analytics, or data engineering. • Experience maintaining technical infrastructure for computing environments supporting AI/ML workloads. • Experience with cloud-native architecture and development methodologies (e.g., modernizing legacy applications, application performance profiling and tuning, container technology). Minimum qualifications: • Bachelor's degree or equivalent practical experience. • 8 years of experience designing, building, and operating large-scale distributed systems, with a focus on scalability, fault tolerance, and reliability. • Experience designing software architecture for applications, including AI/ML systems. • Experience in cloud computing, with a focus on AI/ML, data analytics, or data engineering. • Experience maintaining technical infrastructure for computing environments supporting AI/ML workloads. • Experience with cloud-native architecture and development methodologies (e.g., modernizing legacy applications, application performance profiling and tuning, container technology). Preferred qualifications: • Experience building AI agent harnesses, MCP servers, LLM evaluation pipelines, or neuro-symbolic systems that verify agent infrastructure actions. • Experience with cloud security governance, autonomous remediation, IAM policy automation, VPC Service Controls, OPA, and asset drift detection. • Experience designing large-scale Cloud FinOps, billing attribution, or automated capacity and waste reclamation systems. • Experience with Google Cloud Platform resource management, multi-organization hierarchies, cloud billing, IAM, VPC-SC, and internal production infrastructure. • Experience converging fragmented internal tooling into cohesive, cloud-native developer platforms.

Requirements

Must-haves

Individual pay is determined by factors including job-related skills, experience, and relevant education or training.
Learn more about benefits at Google .
Translate high-level developer and agent intent into deterministic, verifiable environments.
Architect fleet-wide drift detection, formal policy verification, and autonomous IAM/security remediation agents across Alphabet's multi-organization footprint.
Design unified policy and budget decorators so greenfield environments are safe-by-default and cost-governed at creation without manual reviews.
Build high-scale TrueCost billing attribution, ephemeral environment TTL enforcement, and automated brownfield waste reclamation.
Build evaluation harnesses to benchmark agentic infrastructure actuation. Mentor engineers and align technical roadmaps.
Bachelor's degree or equivalent practical experience.
8 years of experience designing, building, and operating large-scale distributed systems, with a focus on scalability, fault tolerance, and reliability.
Experience designing software architecture for applications, including AI/ML systems.
Experience in cloud computing, with a focus on AI/ML, data analytics, or data engineering.
Experience maintaining technical infrastructure for computing environments supporting AI/ML workloads.
Experience with cloud-native architecture and development methodologies (e.g., modernizing legacy applications, application performance profiling and tuning, container technology).
Bachelor's degree or equivalent practical experience.
8 years of experience designing, building, and operating large-scale distributed systems, with a focus on scalability, fault tolerance, and reliability.
Experience designing software architecture for applications, including AI/ML systems.
Experience in cloud computing, with a focus on AI/ML, data analytics, or data engineering.
Experience maintaining technical infrastructure for computing environments supporting AI/ML workloads.

Nice-to-haves

US: $207000 - $300000 (USD) + 20% bonus target + equity + benefits
Experience building AI agent harnesses, MCP servers, LLM evaluation pipelines, or neuro-symbolic systems that verify agent infrastructure actions.
Experience with cloud security governance, autonomous remediation, IAM policy automation, VPC Service Controls, OPA, and asset drift detection.
Experience designing large-scale Cloud FinOps, billing attribution, or automated capacity and waste reclamation systems.
Experience with Google Cloud Platform resource management, multi-organization hierarchies, cloud billing, IAM, VPC-SC, and internal production infrastructure.
Experience converging fragmented internal tooling into cohesive, cloud-native developer platforms.

Application prep

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Keywords to mirror

aigomlllm

Platform: google
Effort: medium