Senior Staff ML Engineer, Ads Content Safety AI/ML

Google LLC; Los Angeles, Mountain View (CA, CA); On-site; USD 262,000 - 364,000; Full time

Job summary

Google LLC in Los Angeles, CA is seeking a Senior Staff Software Engineer for Ads Content Safety AI/ML. You will design, develop, and lead large-scale software solutions powering ads safety across global platforms.

You will provide technical leadership on high-impact ML systems, drive strategy, and oversee ML infrastructure optimization. This role demands deep collaboration with research and engineering teams, and strong architectural judgment to scale models and data pipelines.

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 end-to-end Machine Learning.
• 5 years of experience with one or more of: Speech/audio, reinforcement learning, ML infrastructure, or related ML field.
• 5 years of experience with design and architecture; testing/launching software products.

Responsibilities

• Design, develop, test, deploy, maintain, and enhance large scale software solutions.
• Provide technical leadership on high-impact projects. Manage project priorities, deadlines, and deliverables.
• Facilitate alignment and clarity across teams on goals, outcomes, and timelines. Influence and coach a distributed team of engineers.
• Drive technical project strategy, lead large-scale ML infrastructure optimization, and oversee cross-specialized ML initiatives.

Education

Bachelor’s degree or equivalent practical experience

Master’s degree or PhD in Engineering, Computer Science, or a related technical field

Job description

Google LLC in Los Angeles, CA is seeking a Senior Staff Software Engineer for Ads Content Safety AI/ML. You will design, develop, and lead large-scale software solutions powering ads safety across global platforms.

You will provide technical leadership on high-impact ML systems, drive strategy, and oversee ML infrastructure optimization. This role demands deep collaboration with research and engineering teams, and strong architectural judgment to scale models and data pipelines.