Senior Software Engineer, AI/ML, Ads Bidding

About the role

MINIMUM QUALIFICATIONS:

• Bachelor’s degree or equivalent practical experience.

• 5 years of experience programming in Python or C++.

• 3 years of experience testing, maintaining, or launching software products,

and 1 year of experience with software design and architecture.

• 3 years of experience with end-to-end Machine Learning (e.g., model

deployment, model evaluation, optimization, debugging).

• 3 years of experience with one or more of the following: LLMs,

Multi-Modality, Large Vision Models, or reinforcement learning (e.g.,

sequential decision making).

PREFERRED QUALIFICATIONS:

• Master's degree or PhD in a quantitative field such as Statistics,

Engineering, or Mathematics.

• 5 years of experience with data structures/algorithms.
• 1 year of experience in a technical leadership role.

• Experience developing accessible technologies.

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 build and maintain machine learning models using state-of-the-art AI and ML

techniques to predict user interactions on Search Ads. These models are a key

component in setting advertisers' bids, with the goal of improving both

satisfaction and return on investment for Search Ads advertisers using

Auto-bidding products. By optimizing towards advertisers' objectives,

Auto-bidding products drive Google's global Ads business.

You will be involved in the full machine learning model life-cycle, from design

and training to deployment and serving models in production at the scale of

billions of Search Ads. This role offers the opportunity to innovate while

collaborating with other teams, including research teams, to test and implement

the latest technologies in our models.

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: $174000 - $252000 (USD) + 15% bonus target + equity + benefits

Learn more about benefits at Google

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

RESPONSIBILITIES:

• Write and test product or system development code.

• Collaborate with peers and stakeholders through design and code reviews to

ensure best practices amongst available technologies (e.g., style guidelines,

checking code in, accuracy, testability, and efficiency).

• Contribute to existing documentation or educational content and adapt content

based on product/program updates and user feedback.

• Solve complex machine learning related problems by designing, running, and

analyzing experiments using analytical and statistical methods.

• Innovate and iterate on machine learning model design, improving quality,

stability, and efficiency across the entire model life-cycle—from concept to

deployment.