Senior Staff Software Engineer, AI/ML, Security

Minimum qualifications:

• Bachelor’s degree or equivalent practical experience.
• 8 years of experience in software development.
• 7 years of experience leading technical 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 deep 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 elite 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 - $365000 (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.