Senior Security Engineer, AI Systems Security
Job Summary
Job Description
As a Senior Security Engineer, you'll enhance AI/ML security through vulnerability research, system analysis, and security assessments. You'll collaborate with teams building core ML infrastructure, reviewing designs and implementations. Responsibilities include participating in security design reviews, implementing security improvements, remediating vulnerabilities, designing hardening measures (e.g., sandboxing, secure input processing), developing security tools and automation, and ensuring compliance. The role requires expertise in secure architecture for ML systems and collaboration with engineers across Google's AI and ML infrastructure.
Must have:
- 5+ years security assessment/design review/threat modeling experience
- 5+ years security engineering, computer/network security, security protocols experience
- 5+ years coding experience (general-purpose languages)
- 1+ year leading technical teams or risk analysis
- Experience with secure architecture for ML systems
Good to have:
- Experience with ML frameworks (JAX, TensorFlow), ML infrastructure
- Cloud platforms, distributed systems, or infrastructure experience
- Ability to read, understand, and analyze code; write tools/scripts
Perks:
- Bonus
- Equity
- Benefits
2 skills required
Job Details
Minimum qualifications:
- Bachelor's degree or equivalent practical experience.
- 5 years of experience with security assessments or security design reviews or threat modeling.
- 5 years of experience with security engineering, computer and network security and security protocols.
- 5 years of coding experience in one or more general purpose languages.
- 1 year of experience leading teams in a technical capacity or leading technical risk analysis in an enterprise environment.
Preferred qualifications:
- Experience with machine learning frameworks (e.g., JAX, TensorFlow) or ML infrastructure components.
- Experience with cloud platforms, distributed systems, or infrastructure.
- Knowledge of threat modeling, risk assessments, or security assessments of systems.
- Ability to read, understand, and analyze code as well as write small tools and scripts.
About the job
Our Security team works to create and maintain the safest operating environment for Google's users and developers. Security Engineers work with network equipment and actively monitor our systems for attacks and intrusions. In this role, you will also work with software engineers to proactively identify and fix security flaws and vulnerabilities.
As a Senior Security Engineer in this role, you will focus on enhancing AI/ML security through technical vulnerability research, analysis of systems, and performing security assessments to uncover potential threats. You will collaborate with teams building core ML infrastructure to review designs and implementations, as well as with fellow security engineers.
The US base salary range for this full-time position is $166,000-$244,000 + bonus + equity + benefits. Our salary ranges are determined by role, level, and location. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training. Your recruiter can share more about the specific salary range for your preferred location during the hiring process.Please note that the compensation details listed in US role postings reflect the base salary only, and do not include bonus, equity, or benefits. Learn more about .
Responsibilities
- Participate in security design reviews and provide expert guidance on secure architecture for new and existing ML systems.
- Collaborate with engineers across Google's AI and ML infrastructure teams (e.g., Core ML, GDM) to design and implement security improvements for keystone Libraries, Frameworks, and Services.
- Drive the remediation of vulnerabilities discovered through security reviews, vulnerability research, and incident response.
- Design, develop, and implement practical hardening measures based on threat modeling and security assessments, focusing on areas like workload isolation (sandboxing), memory safety, secure input processing, authentication and authorization, and data protection.
- Develop tools, automation, and secure by default configurations to improve the security posture of ML infrastructure and make compliance easier for developers.
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