Senior Staff Software Engineer, AI/ML

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

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

evaluation, data processing, debugging, fine tuning).

• 5 years of experience with one or more of the following: Speech/audio (e.g.,

technology duplicating and responding to the human voice), reinforcement

learning (e.g., sequential decision making), ML infrastructure, or

specialization in another ML field.

• 5 years of experience with design and architecture; and testing/launching

software products.

• 5 years of experience in Cloud.

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.
• 5 years of experience in a technical leadership role leading project teams

and setting technical direction.

• 5 years of programming experience in Java.
• Experience with AI model security, adversarial machine learning, or data

privacy (e.g., prompt injection defenses, LLM introspection).

• Experience delivering enterprise-grade security products that achieve

measurable, high-impact business and customer outcomes.

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.

As a Senior Staff Software Engineer for AI/ML Security, you will serve as the

primary technical visionary for Model Armor and Sensitive Data Protection,

supporting the strategy to secure LLMs against prompt injection, jailbreaking,

and data exfiltration. You will manage architectural issues by integrating

filter-based defenses into scalable, observable, and explainable distributed

systems that protect enterprise customers at Google Cloud scale. Beyond

technical execution, you will navigate extreme ambiguity to drive overarching

business strategy and lead high-stakes, cross-Google initiatives. You will have

a master-level ability to negotiate and forge consensus among researchers and

engineers, translating security goals into measurable, high-impact solutions for

the generative AI era.

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:

• Define and drive the long-term technical goal and architectural strategy for

model armor and sensitive data protection, ensuring Google Cloud remains a

leader in generative AI security.

• Lead the design and implementation of highly scalable, low-latency systems to

detect and mitigate emerging threats such as prompt injection, jailbreaking,

and sensitive data leakage across enterprise environments.

• Act as a principal technical influencer to align security roadmaps across

Google Cloud, DeepMind, and other product areas, navigating constraints like

latency, cost, and global compliance.

• Resolve technical disagreements among execute engineers and researchers,

forging consensus to deliver on high-stakes, cross-functional objectives

across multiple time zones.

• Partner with executive leadership to evaluate Machine Learning (ML) security

research and provide technical mentorship to staff and executive engineers,

elevating the organization’s overall domain expertise.