Staff ML Software Engineer, Ads Modeling, Personalization, and Quality

About the role

MINIMUM QUALIFICATIONS:

• Bachelor's degree in Computer Science or a related technical field, or

equivalent practical experience.

• 8 years of experience in software development.

• 5 years of experience with ML design and ML infrastructure (e.g., model

deployment, model evaluation, data processing, debugging, fine tuning).

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

of experience with software design and architecture.

• 5 years of experience with ML/AI algorithms and tools, deep learning or other

AI/ML modeling.

• Experience in the digital Ads ecosystem.

PREFERRED QUALIFICATIONS:

• 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 organization involving

cross-functional, or cross-business projects.

• Experience with advertising products, quality work in ads, or with

statistical modeling and experimentation/analysis.

• Experience in quality or modeling engineering teams.

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.

App Ads is one of the fastest growing businesses in Google Ads, with an annual

run rate of over $20B. Our business drives a significant portion of business

growth for Google overall. It affects the lives of users around the world by

helping them find new games, plan vacations, and shop for great agreements or

fun experiences, etc. Our team delivers engineering innovations to improve the

App Ads Quality and provide key technologies to empower App Ads business growth.

We focus on building machine learning models to predict users’ interaction with

ads, and combining them with principled optimization techniques to help

publishers monetize their content and advertisers acquire engaged users.

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 new deep learning based ML models to predict users’ interaction with

different ad formats across Google’s various ad delivery channels.

• Incorporate and engineer input features from ad creatives, publisher

pages/content, as well as users’ personalized ad history to improve models’

accuracy.

• Improve the efficiency and reliability of ML models by designing novel model

architectures and adopting development and release processes.

• Debug and troubleshoot advertiser performance problems.
• Work with Product Management, UX, and partner teams to design and deliver new

ad experiences. Leverage data and metrics to help evaluate product success

and drive business decisions.