Staff Software Engineer, Search Ads Syndication Quality AI/ML

About the role

MINIMUM QUALIFICATIONS:

• Bachelor’s degree or equivalent practical experience.

• 8 years of experience in software development.

• 5 years of experience testing, and launching software products, and 3 years

of experience with software design and architecture.

• 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 leading ML design and optimizing end-to-end Machine

Learning (e.g., model deployment, model evaluation, data processing,

debugging, fine tuning).

• Experience integrating generative AI tools or LLM interfaces into workflows.

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

and setting technical direction.

• 3 years of experience working in a complex, matrixed organization involving

cross-functional, or cross-business projects.

ABOUT THE JOB:

Google's software engineers develop the next-generation technologies that change

how billions of users connect, explore, and interact with information and one

another. Our products need to handle information at massive scale, and extend

well beyond web search. 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’s needs with opportunities to switch teams and projects as

you and our fast-paced business grow and evolve. 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.

We will contribute to Google LTRPM growth through ML/LLM modeling innovations

and scalable serving infrastructure. In addition, leveraging full-stack,

end-to-end expertise, we will agilely lead high-risk exploratory

initiatives—incubating emergent ideas and testing novel architectures, closely

partnering with AdsML.

We will sharpen our focus on ML/LLM modeling and integration to maximize our

long-term, sustainable impact. We will transition away from fragmented efforts

and establish clear ownership of our key workstreams.

Google Ads is at the forefront of AI innovation, applying cutting-edge machine

learning and Generative AI models like Gemini to power a multi-billion dollar

global business.

Our work directly impacts billions of users by protecting users from harm,

improving ad quality, and optimizing campaigns for advertiser

return-on-investment. We foster a culture of deep collaboration, partnering

closely with teams like Google Research and DeepMind to solve complex

challenges. Join us to work on state-of-the-art AI, take on problems at an

unparalleled scale, and build the next generation of advertising technology.

Individual pay is determined by factors including job-related skills,

experience, and relevant education or training.

US: $207000 - $300000 (USD) + 20% bonus target + equity + benefits

Learn more about benefits at Google

[https://www.google.com/about/careers/applications/benefits/].

RESPONSIBILITIES:

• Design, develop, test, deploy, maintain, and enhance large-scale software

solutions.

• Provide technical leadership on 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.

• Lead the design and implementation of solutions in specialized ML areas,

optimize ML infrastructure, and guide the development of model optimization

and data processing strategies.