Senior Staff AI/ML Engineer for Ads and Large-Scale ML

Google; Mountain View (CA); On-site; USD 248,000 - 349,000; Full time

Job summary

Google Ads software engineers develop next-generation technologies that scale to billions of users. This Senior Staff role focuses on AI/ML leadership within Ads, influencing multiple teams and directing ML infrastructure at Google.

You will own outcomes, shape model deployment strategies, and drive architecture across large-scale systems. The successful candidate will lead ML design, optimization, and evaluation efforts, while mentoring engineers and collaborating across organizations to

Qualifications

• Bachelor’s degree or equivalent practical experience.
• 8 years of experience in software development.
• 7 years leading technical project strategy, ML design, and optimizing industry-scale ML infrastructure (model deployment, evaluation, data processing, debugging, fine tuning).
• 5 years of design and architecture; and testing/launching software products.
• 5 years of experience with one or more: Speech/audio, reinforcement learning, ML infrastructure, or another ML field.

Responsibilities

• Design, develop, test, deploy, maintain, and enhance large scale software solutions.
• Provide technical leadership on high-impact projects; manage priorities, deadlines, deliverables.
• Facilitate alignment across teams on goals and timelines; coach distributed engineers.
• Drive ML infrastructure optimization and oversee cross-area solution design and implementation.

Skills

Outcome ownership

Decision making

Ambiguity handling

Stakeholder influence

ML design

Model deployment

System design

Software architecture

Testing/launching products

Education

Bachelor’s degree or equivalent

Master’s degree or PhD (preferred)

Job description

Google Ads software engineers develop next-generation technologies that scale to billions of users. This Senior Staff role focuses on AI/ML leadership within Ads, influencing multiple teams and directing ML infrastructure at Google.

You will own outcomes, shape model deployment strategies, and drive architecture across large-scale systems. The successful candidate will lead ML design, optimization, and evaluation efforts, while mentoring engineers and collaborating across organizations to