Software Engineer, App Ads Bidding and Optimization

About the role

MINIMUM QUALIFICATIONS:

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

equivalent practical experience.

• 2 years of experience with software development in one or more programming

languages, or 1 year of experience with an advanced degree.

• Experience in software development using C++.
• Experience in conducting quantitative data analysis, statistical modeling,

and interpreting datasets.

• Experience in mathematical optimization, and statistical learning.

PREFERRED QUALIFICATIONS:

• Experience in system design, including defining technical architecture,

system interfaces, and data models.

• Experience in programming languages used for data analysis and computational

statistics (e.g., Python, R, MATLAB, C++, Java, or Go).

• Experience with Colab, SQL, and other data analysis tools.
• Experience working with one or more of the following: distributed and

parallel systems, machine learning models, information retrieval, and

mathematical optimization techniques including linear programming, convex

optimization, or reinforcement learning.

• Strong quantitative reasoning skills, demonstrated in a mathematical field

such as data science, optimization, machine learning, or natural science.

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.

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: $147000 - $210000 (USD) + 15% bonus target + equity + benefits

Learn more about benefits at Google

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

RESPONSIBILITIES:

• Improve the accuracy and stability of the UAC bidder with statistical

analysis and new ML/optimization algorithms. Improve/modernize bidding

infrastructure and design and implement bidding algorithms for faster cold

start for new campaigns.

• Debug inaccuracies in bidding infrastructure and suggest improvements and

launch with design, implementation, experimentation and analysis.

• Incorporate and engineer input features from ad creatives, publisher

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

and modeling accuracy.

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

architectures and adopting robust development and release processes.

Debug and troubleshoot complex interactions between business goals,

mathematical models, and infrastructure.

• Work with PM, UX, and partners teams to design and deliver new ad

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

drive business decisions.