Senior Staff Software Engineer, Cloud AI, Agent Governance

About the role

MINIMUM QUALIFICATIONS:

• Bachelor's degree or equivalent practical experience.
• 8 years of experience in software development.

• 7 years of experience leading technical project strategy, ML design, and

optimizing industry-scale ML infrastructure (e.g., model deployment, model

evaluation, data processing, debugging, fine tuning).

• 5 years of experience with design and architecture, and testing, launching

software products.

• 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:

• 10 years of experience leading the design and implementation of scalable

distributed systems, ML platforms, or observability infrastructure, ideally

in the context of large-scale agent platforms.

• Experience leading engineering teams and collaborating with product managers

and researchers to translate scientific concepts into reliable developer

tools.

• Strong understanding of LLM execution mechanics, tool-calling APIs, and

model-level failure analysis.

• Fluency in distributed tracing, semantic monitoring, and telemetry collection

at scale.

• Proven track record of shipping complex enterprise-grade developer platforms,

virtualization/sandbox environments, or production-grade test harnesses.

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.

With your technical expertise you will manage project priorities, deadlines, and

deliverables. You will design, develop, test, deploy, maintain, and enhance

software solutions.

Google Cloud is building the most comprehensive AI platform in the industry. As

platform adoption scales, enterprise customers require stable, highly scalable,

and completely secure simulation environments and evaluation infrastructure to

operationalize autonomous agents. This role focuses on the systems and platform

engineering required to close the last mile of agentic governance for enterprise

deployments.

You will lead the delivery of the engineering roadmap, integrating components

from multiple product teams to build an intuitive governance user experience for

CISOs, auditors, and enterprise IT teams.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: $262000 - $364000 (USD) + 25% bonus target + equity + benefits

Learn more about benefits at Google

[https://www.google.com/about/careers/applications/benefits/].

RESPONSIBILITIES:

• Architect, build, and operate large-scale environments that allow developers

to test complex agent behaviors, tool calling, multi-turn workflows, and

end-to-end workflows in isolated, reproducible sandboxes.

• Deliver reproducible evaluation infrastructure and automated test harnesses

capable of validating against enteprise visibility, security, compliance

requirements.

• Design and scale the platform infrastructure that enables agentic systems to

validate online and audit offline and alert based on behavioral patterns.

• Build the metrics engines and dashboards to track evaluation coverage,

semantic correctness, quality metrics, and regression signals over time.

Standardize the use of metrics for production health.

• Integrate evaluation runs into deployment pipelines, creating release gates

to prevent semantic and structural regressions before agent deployments reach

production.